In [1]:
import numpy as np
import pandas as pd
import matplotlib
from matplotlib import pyplot as plt
import seaborn as sns
import pylab
from pylab import legend, plot, show, title, xlabel, ylabel
import scipy
from scipy import stats
from scipy.stats import binom, poisson, norm, t
In [2]:
application_df = pd.read_csv('D:\\DBDA_Modules\\Credit EDA Case Study\\application_data.csv')
In [3]:
application_df.head()
# first 5 rows of aapplication data
Out[3]:
| SK_ID_CURR | TARGET | NAME_CONTRACT_TYPE | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | ... | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 100002 | 1 | Cash loans | M | N | Y | 0 | 202500.0 | 406597.5 | 24700.5 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 1 | 100003 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2 | 100004 | 0 | Revolving loans | M | Y | Y | 0 | 67500.0 | 135000.0 | 6750.0 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3 | 100006 | 0 | Cash loans | F | N | Y | 0 | 135000.0 | 312682.5 | 29686.5 | ... | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 100007 | 0 | Cash loans | M | N | Y | 0 | 121500.0 | 513000.0 | 21865.5 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
5 rows × 122 columns
In [4]:
application_df.tail()
# checking the last rows are completely null or not
Out[4]:
| SK_ID_CURR | TARGET | NAME_CONTRACT_TYPE | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | ... | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 307506 | 456251 | 0 | Cash loans | M | N | N | 0 | 157500.0 | 254700.0 | 27558.0 | ... | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 307507 | 456252 | 0 | Cash loans | F | N | Y | 0 | 72000.0 | 269550.0 | 12001.5 | ... | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 307508 | 456253 | 0 | Cash loans | F | N | Y | 0 | 153000.0 | 677664.0 | 29979.0 | ... | 0 | 0 | 0 | 0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 |
| 307509 | 456254 | 1 | Cash loans | F | N | Y | 0 | 171000.0 | 370107.0 | 20205.0 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 307510 | 456255 | 0 | Cash loans | F | N | N | 0 | 157500.0 | 675000.0 | 49117.5 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 1.0 |
5 rows × 122 columns
In [5]:
pd.set_option('display.max_columns', None)
# to display all the columns without skipping any
application_df.shape
# there are 307511 rows and 122 columns
Out[5]:
(307511, 122)
In [6]:
application_df.info(verbose=True)
# to display all columns woth datatypes
<class 'pandas.core.frame.DataFrame'> RangeIndex: 307511 entries, 0 to 307510 Data columns (total 122 columns): # Column Dtype --- ------ ----- 0 SK_ID_CURR int64 1 TARGET int64 2 NAME_CONTRACT_TYPE object 3 CODE_GENDER object 4 FLAG_OWN_CAR object 5 FLAG_OWN_REALTY object 6 CNT_CHILDREN int64 7 AMT_INCOME_TOTAL float64 8 AMT_CREDIT float64 9 AMT_ANNUITY float64 10 AMT_GOODS_PRICE float64 11 NAME_TYPE_SUITE object 12 NAME_INCOME_TYPE object 13 NAME_EDUCATION_TYPE object 14 NAME_FAMILY_STATUS object 15 NAME_HOUSING_TYPE object 16 REGION_POPULATION_RELATIVE float64 17 DAYS_BIRTH int64 18 DAYS_EMPLOYED int64 19 DAYS_REGISTRATION float64 20 DAYS_ID_PUBLISH int64 21 OWN_CAR_AGE float64 22 FLAG_MOBIL int64 23 FLAG_EMP_PHONE int64 24 FLAG_WORK_PHONE int64 25 FLAG_CONT_MOBILE int64 26 FLAG_PHONE int64 27 FLAG_EMAIL int64 28 OCCUPATION_TYPE object 29 CNT_FAM_MEMBERS float64 30 REGION_RATING_CLIENT int64 31 REGION_RATING_CLIENT_W_CITY int64 32 WEEKDAY_APPR_PROCESS_START object 33 HOUR_APPR_PROCESS_START int64 34 REG_REGION_NOT_LIVE_REGION int64 35 REG_REGION_NOT_WORK_REGION int64 36 LIVE_REGION_NOT_WORK_REGION int64 37 REG_CITY_NOT_LIVE_CITY int64 38 REG_CITY_NOT_WORK_CITY int64 39 LIVE_CITY_NOT_WORK_CITY int64 40 ORGANIZATION_TYPE object 41 EXT_SOURCE_1 float64 42 EXT_SOURCE_2 float64 43 EXT_SOURCE_3 float64 44 APARTMENTS_AVG float64 45 BASEMENTAREA_AVG float64 46 YEARS_BEGINEXPLUATATION_AVG float64 47 YEARS_BUILD_AVG float64 48 COMMONAREA_AVG float64 49 ELEVATORS_AVG float64 50 ENTRANCES_AVG float64 51 FLOORSMAX_AVG float64 52 FLOORSMIN_AVG float64 53 LANDAREA_AVG float64 54 LIVINGAPARTMENTS_AVG float64 55 LIVINGAREA_AVG float64 56 NONLIVINGAPARTMENTS_AVG float64 57 NONLIVINGAREA_AVG float64 58 APARTMENTS_MODE float64 59 BASEMENTAREA_MODE float64 60 YEARS_BEGINEXPLUATATION_MODE float64 61 YEARS_BUILD_MODE float64 62 COMMONAREA_MODE float64 63 ELEVATORS_MODE float64 64 ENTRANCES_MODE float64 65 FLOORSMAX_MODE float64 66 FLOORSMIN_MODE float64 67 LANDAREA_MODE float64 68 LIVINGAPARTMENTS_MODE float64 69 LIVINGAREA_MODE float64 70 NONLIVINGAPARTMENTS_MODE float64 71 NONLIVINGAREA_MODE float64 72 APARTMENTS_MEDI float64 73 BASEMENTAREA_MEDI float64 74 YEARS_BEGINEXPLUATATION_MEDI float64 75 YEARS_BUILD_MEDI float64 76 COMMONAREA_MEDI float64 77 ELEVATORS_MEDI float64 78 ENTRANCES_MEDI float64 79 FLOORSMAX_MEDI float64 80 FLOORSMIN_MEDI float64 81 LANDAREA_MEDI float64 82 LIVINGAPARTMENTS_MEDI float64 83 LIVINGAREA_MEDI float64 84 NONLIVINGAPARTMENTS_MEDI float64 85 NONLIVINGAREA_MEDI float64 86 FONDKAPREMONT_MODE object 87 HOUSETYPE_MODE object 88 TOTALAREA_MODE float64 89 WALLSMATERIAL_MODE object 90 EMERGENCYSTATE_MODE object 91 OBS_30_CNT_SOCIAL_CIRCLE float64 92 DEF_30_CNT_SOCIAL_CIRCLE float64 93 OBS_60_CNT_SOCIAL_CIRCLE float64 94 DEF_60_CNT_SOCIAL_CIRCLE float64 95 DAYS_LAST_PHONE_CHANGE float64 96 FLAG_DOCUMENT_2 int64 97 FLAG_DOCUMENT_3 int64 98 FLAG_DOCUMENT_4 int64 99 FLAG_DOCUMENT_5 int64 100 FLAG_DOCUMENT_6 int64 101 FLAG_DOCUMENT_7 int64 102 FLAG_DOCUMENT_8 int64 103 FLAG_DOCUMENT_9 int64 104 FLAG_DOCUMENT_10 int64 105 FLAG_DOCUMENT_11 int64 106 FLAG_DOCUMENT_12 int64 107 FLAG_DOCUMENT_13 int64 108 FLAG_DOCUMENT_14 int64 109 FLAG_DOCUMENT_15 int64 110 FLAG_DOCUMENT_16 int64 111 FLAG_DOCUMENT_17 int64 112 FLAG_DOCUMENT_18 int64 113 FLAG_DOCUMENT_19 int64 114 FLAG_DOCUMENT_20 int64 115 FLAG_DOCUMENT_21 int64 116 AMT_REQ_CREDIT_BUREAU_HOUR float64 117 AMT_REQ_CREDIT_BUREAU_DAY float64 118 AMT_REQ_CREDIT_BUREAU_WEEK float64 119 AMT_REQ_CREDIT_BUREAU_MON float64 120 AMT_REQ_CREDIT_BUREAU_QRT float64 121 AMT_REQ_CREDIT_BUREAU_YEAR float64 dtypes: float64(65), int64(41), object(16) memory usage: 286.2+ MB
In [7]:
# checking the statistical summary
numerical_columns = application_df.select_dtypes(include=['float64', 'int64'])
print(numerical_columns.describe())
SK_ID_CURR TARGET CNT_CHILDREN AMT_INCOME_TOTAL \
count 307511.000000 307511.000000 307511.000000 3.075110e+05
mean 278180.518577 0.080729 0.417052 1.687979e+05
std 102790.175348 0.272419 0.722121 2.371231e+05
min 100002.000000 0.000000 0.000000 2.565000e+04
25% 189145.500000 0.000000 0.000000 1.125000e+05
50% 278202.000000 0.000000 0.000000 1.471500e+05
75% 367142.500000 0.000000 1.000000 2.025000e+05
max 456255.000000 1.000000 19.000000 1.170000e+08
AMT_CREDIT AMT_ANNUITY AMT_GOODS_PRICE \
count 3.075110e+05 307499.000000 3.072330e+05
mean 5.990260e+05 27108.573909 5.383962e+05
std 4.024908e+05 14493.737315 3.694465e+05
min 4.500000e+04 1615.500000 4.050000e+04
25% 2.700000e+05 16524.000000 2.385000e+05
50% 5.135310e+05 24903.000000 4.500000e+05
75% 8.086500e+05 34596.000000 6.795000e+05
max 4.050000e+06 258025.500000 4.050000e+06
REGION_POPULATION_RELATIVE DAYS_BIRTH DAYS_EMPLOYED \
count 307511.000000 307511.000000 307511.000000
mean 0.020868 -16036.995067 63815.045904
std 0.013831 4363.988632 141275.766519
min 0.000290 -25229.000000 -17912.000000
25% 0.010006 -19682.000000 -2760.000000
50% 0.018850 -15750.000000 -1213.000000
75% 0.028663 -12413.000000 -289.000000
max 0.072508 -7489.000000 365243.000000
DAYS_REGISTRATION DAYS_ID_PUBLISH OWN_CAR_AGE FLAG_MOBIL \
count 307511.000000 307511.000000 104582.000000 307511.000000
mean -4986.120328 -2994.202373 12.061091 0.999997
std 3522.886321 1509.450419 11.944812 0.001803
min -24672.000000 -7197.000000 0.000000 0.000000
25% -7479.500000 -4299.000000 5.000000 1.000000
50% -4504.000000 -3254.000000 9.000000 1.000000
75% -2010.000000 -1720.000000 15.000000 1.000000
max 0.000000 0.000000 91.000000 1.000000
FLAG_EMP_PHONE FLAG_WORK_PHONE FLAG_CONT_MOBILE FLAG_PHONE \
count 307511.000000 307511.000000 307511.000000 307511.000000
mean 0.819889 0.199368 0.998133 0.281066
std 0.384280 0.399526 0.043164 0.449521
min 0.000000 0.000000 0.000000 0.000000
25% 1.000000 0.000000 1.000000 0.000000
50% 1.000000 0.000000 1.000000 0.000000
75% 1.000000 0.000000 1.000000 1.000000
max 1.000000 1.000000 1.000000 1.000000
FLAG_EMAIL CNT_FAM_MEMBERS REGION_RATING_CLIENT \
count 307511.000000 307509.000000 307511.000000
mean 0.056720 2.152665 2.052463
std 0.231307 0.910682 0.509034
min 0.000000 1.000000 1.000000
25% 0.000000 2.000000 2.000000
50% 0.000000 2.000000 2.000000
75% 0.000000 3.000000 2.000000
max 1.000000 20.000000 3.000000
REGION_RATING_CLIENT_W_CITY HOUR_APPR_PROCESS_START \
count 307511.000000 307511.000000
mean 2.031521 12.063419
std 0.502737 3.265832
min 1.000000 0.000000
25% 2.000000 10.000000
50% 2.000000 12.000000
75% 2.000000 14.000000
max 3.000000 23.000000
REG_REGION_NOT_LIVE_REGION REG_REGION_NOT_WORK_REGION \
count 307511.000000 307511.000000
mean 0.015144 0.050769
std 0.122126 0.219526
min 0.000000 0.000000
25% 0.000000 0.000000
50% 0.000000 0.000000
75% 0.000000 0.000000
max 1.000000 1.000000
LIVE_REGION_NOT_WORK_REGION REG_CITY_NOT_LIVE_CITY \
count 307511.000000 307511.000000
mean 0.040659 0.078173
std 0.197499 0.268444
min 0.000000 0.000000
25% 0.000000 0.000000
50% 0.000000 0.000000
75% 0.000000 0.000000
max 1.000000 1.000000
REG_CITY_NOT_WORK_CITY LIVE_CITY_NOT_WORK_CITY EXT_SOURCE_1 \
count 307511.000000 307511.000000 134133.000000
mean 0.230454 0.179555 0.502130
std 0.421124 0.383817 0.211062
min 0.000000 0.000000 0.014568
25% 0.000000 0.000000 0.334007
50% 0.000000 0.000000 0.505998
75% 0.000000 0.000000 0.675053
max 1.000000 1.000000 0.962693
EXT_SOURCE_2 EXT_SOURCE_3 APARTMENTS_AVG BASEMENTAREA_AVG \
count 3.068510e+05 246546.000000 151450.00000 127568.000000
mean 5.143927e-01 0.510853 0.11744 0.088442
std 1.910602e-01 0.194844 0.10824 0.082438
min 8.173617e-08 0.000527 0.00000 0.000000
25% 3.924574e-01 0.370650 0.05770 0.044200
50% 5.659614e-01 0.535276 0.08760 0.076300
75% 6.636171e-01 0.669057 0.14850 0.112200
max 8.549997e-01 0.896010 1.00000 1.000000
YEARS_BEGINEXPLUATATION_AVG YEARS_BUILD_AVG COMMONAREA_AVG \
count 157504.000000 103023.000000 92646.000000
mean 0.977735 0.752471 0.044621
std 0.059223 0.113280 0.076036
min 0.000000 0.000000 0.000000
25% 0.976700 0.687200 0.007800
50% 0.981600 0.755200 0.021100
75% 0.986600 0.823200 0.051500
max 1.000000 1.000000 1.000000
ELEVATORS_AVG ENTRANCES_AVG FLOORSMAX_AVG FLOORSMIN_AVG \
count 143620.000000 152683.000000 154491.000000 98869.000000
mean 0.078942 0.149725 0.226282 0.231894
std 0.134576 0.100049 0.144641 0.161380
min 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.069000 0.166700 0.083300
50% 0.000000 0.137900 0.166700 0.208300
75% 0.120000 0.206900 0.333300 0.375000
max 1.000000 1.000000 1.000000 1.000000
LANDAREA_AVG LIVINGAPARTMENTS_AVG LIVINGAREA_AVG \
count 124921.000000 97312.000000 153161.000000
mean 0.066333 0.100775 0.107399
std 0.081184 0.092576 0.110565
min 0.000000 0.000000 0.000000
25% 0.018700 0.050400 0.045300
50% 0.048100 0.075600 0.074500
75% 0.085600 0.121000 0.129900
max 1.000000 1.000000 1.000000
NONLIVINGAPARTMENTS_AVG NONLIVINGAREA_AVG APARTMENTS_MODE \
count 93997.000000 137829.000000 151450.000000
mean 0.008809 0.028358 0.114231
std 0.047732 0.069523 0.107936
min 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.052500
50% 0.000000 0.003600 0.084000
75% 0.003900 0.027700 0.143900
max 1.000000 1.000000 1.000000
BASEMENTAREA_MODE YEARS_BEGINEXPLUATATION_MODE YEARS_BUILD_MODE \
count 127568.000000 157504.000000 103023.000000
mean 0.087543 0.977065 0.759637
std 0.084307 0.064575 0.110111
min 0.000000 0.000000 0.000000
25% 0.040700 0.976700 0.699400
50% 0.074600 0.981600 0.764800
75% 0.112400 0.986600 0.823600
max 1.000000 1.000000 1.000000
COMMONAREA_MODE ELEVATORS_MODE ENTRANCES_MODE FLOORSMAX_MODE \
count 92646.000000 143620.000000 152683.000000 154491.000000
mean 0.042553 0.074490 0.145193 0.222315
std 0.074445 0.132256 0.100977 0.143709
min 0.000000 0.000000 0.000000 0.000000
25% 0.007200 0.000000 0.069000 0.166700
50% 0.019000 0.000000 0.137900 0.166700
75% 0.049000 0.120800 0.206900 0.333300
max 1.000000 1.000000 1.000000 1.000000
FLOORSMIN_MODE LANDAREA_MODE LIVINGAPARTMENTS_MODE LIVINGAREA_MODE \
count 98869.000000 124921.000000 97312.000000 153161.000000
mean 0.228058 0.064958 0.105645 0.105975
std 0.161160 0.081750 0.097880 0.111845
min 0.000000 0.000000 0.000000 0.000000
25% 0.083300 0.016600 0.054200 0.042700
50% 0.208300 0.045800 0.077100 0.073100
75% 0.375000 0.084100 0.131300 0.125200
max 1.000000 1.000000 1.000000 1.000000
NONLIVINGAPARTMENTS_MODE NONLIVINGAREA_MODE APARTMENTS_MEDI \
count 93997.000000 137829.000000 151450.000000
mean 0.008076 0.027022 0.117850
std 0.046276 0.070254 0.109076
min 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.058300
50% 0.000000 0.001100 0.086400
75% 0.003900 0.023100 0.148900
max 1.000000 1.000000 1.000000
BASEMENTAREA_MEDI YEARS_BEGINEXPLUATATION_MEDI YEARS_BUILD_MEDI \
count 127568.000000 157504.000000 103023.000000
mean 0.087955 0.977752 0.755746
std 0.082179 0.059897 0.112066
min 0.000000 0.000000 0.000000
25% 0.043700 0.976700 0.691400
50% 0.075800 0.981600 0.758500
75% 0.111600 0.986600 0.825600
max 1.000000 1.000000 1.000000
COMMONAREA_MEDI ELEVATORS_MEDI ENTRANCES_MEDI FLOORSMAX_MEDI \
count 92646.000000 143620.000000 152683.000000 154491.000000
mean 0.044595 0.078078 0.149213 0.225897
std 0.076144 0.134467 0.100368 0.145067
min 0.000000 0.000000 0.000000 0.000000
25% 0.007900 0.000000 0.069000 0.166700
50% 0.020800 0.000000 0.137900 0.166700
75% 0.051300 0.120000 0.206900 0.333300
max 1.000000 1.000000 1.000000 1.000000
FLOORSMIN_MEDI LANDAREA_MEDI LIVINGAPARTMENTS_MEDI LIVINGAREA_MEDI \
count 98869.000000 124921.000000 97312.000000 153161.000000
mean 0.231625 0.067169 0.101954 0.108607
std 0.161934 0.082167 0.093642 0.112260
min 0.000000 0.000000 0.000000 0.000000
25% 0.083300 0.018700 0.051300 0.045700
50% 0.208300 0.048700 0.076100 0.074900
75% 0.375000 0.086800 0.123100 0.130300
max 1.000000 1.000000 1.000000 1.000000
NONLIVINGAPARTMENTS_MEDI NONLIVINGAREA_MEDI TOTALAREA_MODE \
count 93997.000000 137829.000000 159080.000000
mean 0.008651 0.028236 0.102547
std 0.047415 0.070166 0.107462
min 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.041200
50% 0.000000 0.003100 0.068800
75% 0.003900 0.026600 0.127600
max 1.000000 1.000000 1.000000
OBS_30_CNT_SOCIAL_CIRCLE DEF_30_CNT_SOCIAL_CIRCLE \
count 306490.000000 306490.000000
mean 1.422245 0.143421
std 2.400989 0.446698
min 0.000000 0.000000
25% 0.000000 0.000000
50% 0.000000 0.000000
75% 2.000000 0.000000
max 348.000000 34.000000
OBS_60_CNT_SOCIAL_CIRCLE DEF_60_CNT_SOCIAL_CIRCLE \
count 306490.000000 306490.000000
mean 1.405292 0.100049
std 2.379803 0.362291
min 0.000000 0.000000
25% 0.000000 0.000000
50% 0.000000 0.000000
75% 2.000000 0.000000
max 344.000000 24.000000
DAYS_LAST_PHONE_CHANGE FLAG_DOCUMENT_2 FLAG_DOCUMENT_3 \
count 307510.000000 307511.000000 307511.000000
mean -962.858788 0.000042 0.710023
std 826.808487 0.006502 0.453752
min -4292.000000 0.000000 0.000000
25% -1570.000000 0.000000 0.000000
50% -757.000000 0.000000 1.000000
75% -274.000000 0.000000 1.000000
max 0.000000 1.000000 1.000000
FLAG_DOCUMENT_4 FLAG_DOCUMENT_5 FLAG_DOCUMENT_6 FLAG_DOCUMENT_7 \
count 307511.000000 307511.000000 307511.000000 307511.000000
mean 0.000081 0.015115 0.088055 0.000192
std 0.009016 0.122010 0.283376 0.013850
min 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000
75% 0.000000 0.000000 0.000000 0.000000
max 1.000000 1.000000 1.000000 1.000000
FLAG_DOCUMENT_8 FLAG_DOCUMENT_9 FLAG_DOCUMENT_10 FLAG_DOCUMENT_11 \
count 307511.000000 307511.000000 307511.000000 307511.000000
mean 0.081376 0.003896 0.000023 0.003912
std 0.273412 0.062295 0.004771 0.062424
min 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000
75% 0.000000 0.000000 0.000000 0.000000
max 1.000000 1.000000 1.000000 1.000000
FLAG_DOCUMENT_12 FLAG_DOCUMENT_13 FLAG_DOCUMENT_14 FLAG_DOCUMENT_15 \
count 307511.000000 307511.000000 307511.000000 307511.00000
mean 0.000007 0.003525 0.002936 0.00121
std 0.002550 0.059268 0.054110 0.03476
min 0.000000 0.000000 0.000000 0.00000
25% 0.000000 0.000000 0.000000 0.00000
50% 0.000000 0.000000 0.000000 0.00000
75% 0.000000 0.000000 0.000000 0.00000
max 1.000000 1.000000 1.000000 1.00000
FLAG_DOCUMENT_16 FLAG_DOCUMENT_17 FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 \
count 307511.000000 307511.000000 307511.000000 307511.000000
mean 0.009928 0.000267 0.008130 0.000595
std 0.099144 0.016327 0.089798 0.024387
min 0.000000 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000 0.000000
75% 0.000000 0.000000 0.000000 0.000000
max 1.000000 1.000000 1.000000 1.000000
FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR \
count 307511.000000 307511.000000 265992.000000
mean 0.000507 0.000335 0.006402
std 0.022518 0.018299 0.083849
min 0.000000 0.000000 0.000000
25% 0.000000 0.000000 0.000000
50% 0.000000 0.000000 0.000000
75% 0.000000 0.000000 0.000000
max 1.000000 1.000000 4.000000
AMT_REQ_CREDIT_BUREAU_DAY AMT_REQ_CREDIT_BUREAU_WEEK \
count 265992.000000 265992.000000
mean 0.007000 0.034362
std 0.110757 0.204685
min 0.000000 0.000000
25% 0.000000 0.000000
50% 0.000000 0.000000
75% 0.000000 0.000000
max 9.000000 8.000000
AMT_REQ_CREDIT_BUREAU_MON AMT_REQ_CREDIT_BUREAU_QRT \
count 265992.000000 265992.000000
mean 0.267395 0.265474
std 0.916002 0.794056
min 0.000000 0.000000
25% 0.000000 0.000000
50% 0.000000 0.000000
75% 0.000000 0.000000
max 27.000000 261.000000
AMT_REQ_CREDIT_BUREAU_YEAR
count 265992.000000
mean 1.899974
std 1.869295
min 0.000000
25% 0.000000
50% 1.000000
75% 3.000000
max 25.000000
In [8]:
pd.set_option('display.max_rows', None)
# to show all rows without skipping any rows
application_df.isnull().sum().sort_values(ascending=False)
Out[8]:
COMMONAREA_MEDI 214865 COMMONAREA_AVG 214865 COMMONAREA_MODE 214865 NONLIVINGAPARTMENTS_MODE 213514 NONLIVINGAPARTMENTS_AVG 213514 NONLIVINGAPARTMENTS_MEDI 213514 FONDKAPREMONT_MODE 210295 LIVINGAPARTMENTS_MODE 210199 LIVINGAPARTMENTS_AVG 210199 LIVINGAPARTMENTS_MEDI 210199 FLOORSMIN_AVG 208642 FLOORSMIN_MODE 208642 FLOORSMIN_MEDI 208642 YEARS_BUILD_MEDI 204488 YEARS_BUILD_MODE 204488 YEARS_BUILD_AVG 204488 OWN_CAR_AGE 202929 LANDAREA_MEDI 182590 LANDAREA_MODE 182590 LANDAREA_AVG 182590 BASEMENTAREA_MEDI 179943 BASEMENTAREA_AVG 179943 BASEMENTAREA_MODE 179943 EXT_SOURCE_1 173378 NONLIVINGAREA_MODE 169682 NONLIVINGAREA_AVG 169682 NONLIVINGAREA_MEDI 169682 ELEVATORS_MEDI 163891 ELEVATORS_AVG 163891 ELEVATORS_MODE 163891 WALLSMATERIAL_MODE 156341 APARTMENTS_MEDI 156061 APARTMENTS_AVG 156061 APARTMENTS_MODE 156061 ENTRANCES_MEDI 154828 ENTRANCES_AVG 154828 ENTRANCES_MODE 154828 LIVINGAREA_AVG 154350 LIVINGAREA_MODE 154350 LIVINGAREA_MEDI 154350 HOUSETYPE_MODE 154297 FLOORSMAX_MODE 153020 FLOORSMAX_MEDI 153020 FLOORSMAX_AVG 153020 YEARS_BEGINEXPLUATATION_MODE 150007 YEARS_BEGINEXPLUATATION_MEDI 150007 YEARS_BEGINEXPLUATATION_AVG 150007 TOTALAREA_MODE 148431 EMERGENCYSTATE_MODE 145755 OCCUPATION_TYPE 96391 EXT_SOURCE_3 60965 AMT_REQ_CREDIT_BUREAU_HOUR 41519 AMT_REQ_CREDIT_BUREAU_DAY 41519 AMT_REQ_CREDIT_BUREAU_WEEK 41519 AMT_REQ_CREDIT_BUREAU_MON 41519 AMT_REQ_CREDIT_BUREAU_QRT 41519 AMT_REQ_CREDIT_BUREAU_YEAR 41519 NAME_TYPE_SUITE 1292 OBS_30_CNT_SOCIAL_CIRCLE 1021 DEF_30_CNT_SOCIAL_CIRCLE 1021 OBS_60_CNT_SOCIAL_CIRCLE 1021 DEF_60_CNT_SOCIAL_CIRCLE 1021 EXT_SOURCE_2 660 AMT_GOODS_PRICE 278 AMT_ANNUITY 12 CNT_FAM_MEMBERS 2 DAYS_LAST_PHONE_CHANGE 1 CNT_CHILDREN 0 FLAG_DOCUMENT_8 0 NAME_CONTRACT_TYPE 0 CODE_GENDER 0 FLAG_OWN_CAR 0 FLAG_DOCUMENT_2 0 FLAG_DOCUMENT_3 0 FLAG_DOCUMENT_4 0 FLAG_DOCUMENT_5 0 FLAG_DOCUMENT_6 0 FLAG_DOCUMENT_7 0 FLAG_DOCUMENT_9 0 FLAG_DOCUMENT_21 0 FLAG_DOCUMENT_10 0 FLAG_DOCUMENT_11 0 FLAG_OWN_REALTY 0 FLAG_DOCUMENT_13 0 FLAG_DOCUMENT_14 0 FLAG_DOCUMENT_15 0 FLAG_DOCUMENT_16 0 FLAG_DOCUMENT_17 0 FLAG_DOCUMENT_18 0 FLAG_DOCUMENT_19 0 FLAG_DOCUMENT_20 0 FLAG_DOCUMENT_12 0 AMT_CREDIT 0 AMT_INCOME_TOTAL 0 FLAG_PHONE 0 LIVE_CITY_NOT_WORK_CITY 0 REG_CITY_NOT_WORK_CITY 0 TARGET 0 REG_CITY_NOT_LIVE_CITY 0 LIVE_REGION_NOT_WORK_REGION 0 REG_REGION_NOT_WORK_REGION 0 REG_REGION_NOT_LIVE_REGION 0 HOUR_APPR_PROCESS_START 0 WEEKDAY_APPR_PROCESS_START 0 REGION_RATING_CLIENT_W_CITY 0 REGION_RATING_CLIENT 0 FLAG_EMAIL 0 FLAG_CONT_MOBILE 0 ORGANIZATION_TYPE 0 FLAG_WORK_PHONE 0 FLAG_EMP_PHONE 0 FLAG_MOBIL 0 DAYS_ID_PUBLISH 0 DAYS_REGISTRATION 0 DAYS_EMPLOYED 0 DAYS_BIRTH 0 REGION_POPULATION_RELATIVE 0 NAME_HOUSING_TYPE 0 NAME_FAMILY_STATUS 0 NAME_EDUCATION_TYPE 0 NAME_INCOME_TYPE 0 SK_ID_CURR 0 dtype: int64
In [9]:
# to see the percentage of missing values
(application_df.isnull().sum()/len(application_df)*100).sort_values(ascending=False)
Out[9]:
COMMONAREA_MEDI 69.872297 COMMONAREA_AVG 69.872297 COMMONAREA_MODE 69.872297 NONLIVINGAPARTMENTS_MODE 69.432963 NONLIVINGAPARTMENTS_AVG 69.432963 NONLIVINGAPARTMENTS_MEDI 69.432963 FONDKAPREMONT_MODE 68.386172 LIVINGAPARTMENTS_MODE 68.354953 LIVINGAPARTMENTS_AVG 68.354953 LIVINGAPARTMENTS_MEDI 68.354953 FLOORSMIN_AVG 67.848630 FLOORSMIN_MODE 67.848630 FLOORSMIN_MEDI 67.848630 YEARS_BUILD_MEDI 66.497784 YEARS_BUILD_MODE 66.497784 YEARS_BUILD_AVG 66.497784 OWN_CAR_AGE 65.990810 LANDAREA_MEDI 59.376738 LANDAREA_MODE 59.376738 LANDAREA_AVG 59.376738 BASEMENTAREA_MEDI 58.515956 BASEMENTAREA_AVG 58.515956 BASEMENTAREA_MODE 58.515956 EXT_SOURCE_1 56.381073 NONLIVINGAREA_MODE 55.179164 NONLIVINGAREA_AVG 55.179164 NONLIVINGAREA_MEDI 55.179164 ELEVATORS_MEDI 53.295980 ELEVATORS_AVG 53.295980 ELEVATORS_MODE 53.295980 WALLSMATERIAL_MODE 50.840783 APARTMENTS_MEDI 50.749729 APARTMENTS_AVG 50.749729 APARTMENTS_MODE 50.749729 ENTRANCES_MEDI 50.348768 ENTRANCES_AVG 50.348768 ENTRANCES_MODE 50.348768 LIVINGAREA_AVG 50.193326 LIVINGAREA_MODE 50.193326 LIVINGAREA_MEDI 50.193326 HOUSETYPE_MODE 50.176091 FLOORSMAX_MODE 49.760822 FLOORSMAX_MEDI 49.760822 FLOORSMAX_AVG 49.760822 YEARS_BEGINEXPLUATATION_MODE 48.781019 YEARS_BEGINEXPLUATATION_MEDI 48.781019 YEARS_BEGINEXPLUATATION_AVG 48.781019 TOTALAREA_MODE 48.268517 EMERGENCYSTATE_MODE 47.398304 OCCUPATION_TYPE 31.345545 EXT_SOURCE_3 19.825307 AMT_REQ_CREDIT_BUREAU_HOUR 13.501631 AMT_REQ_CREDIT_BUREAU_DAY 13.501631 AMT_REQ_CREDIT_BUREAU_WEEK 13.501631 AMT_REQ_CREDIT_BUREAU_MON 13.501631 AMT_REQ_CREDIT_BUREAU_QRT 13.501631 AMT_REQ_CREDIT_BUREAU_YEAR 13.501631 NAME_TYPE_SUITE 0.420148 OBS_30_CNT_SOCIAL_CIRCLE 0.332021 DEF_30_CNT_SOCIAL_CIRCLE 0.332021 OBS_60_CNT_SOCIAL_CIRCLE 0.332021 DEF_60_CNT_SOCIAL_CIRCLE 0.332021 EXT_SOURCE_2 0.214626 AMT_GOODS_PRICE 0.090403 AMT_ANNUITY 0.003902 CNT_FAM_MEMBERS 0.000650 DAYS_LAST_PHONE_CHANGE 0.000325 CNT_CHILDREN 0.000000 FLAG_DOCUMENT_8 0.000000 NAME_CONTRACT_TYPE 0.000000 CODE_GENDER 0.000000 FLAG_OWN_CAR 0.000000 FLAG_DOCUMENT_2 0.000000 FLAG_DOCUMENT_3 0.000000 FLAG_DOCUMENT_4 0.000000 FLAG_DOCUMENT_5 0.000000 FLAG_DOCUMENT_6 0.000000 FLAG_DOCUMENT_7 0.000000 FLAG_DOCUMENT_9 0.000000 FLAG_DOCUMENT_21 0.000000 FLAG_DOCUMENT_10 0.000000 FLAG_DOCUMENT_11 0.000000 FLAG_OWN_REALTY 0.000000 FLAG_DOCUMENT_13 0.000000 FLAG_DOCUMENT_14 0.000000 FLAG_DOCUMENT_15 0.000000 FLAG_DOCUMENT_16 0.000000 FLAG_DOCUMENT_17 0.000000 FLAG_DOCUMENT_18 0.000000 FLAG_DOCUMENT_19 0.000000 FLAG_DOCUMENT_20 0.000000 FLAG_DOCUMENT_12 0.000000 AMT_CREDIT 0.000000 AMT_INCOME_TOTAL 0.000000 FLAG_PHONE 0.000000 LIVE_CITY_NOT_WORK_CITY 0.000000 REG_CITY_NOT_WORK_CITY 0.000000 TARGET 0.000000 REG_CITY_NOT_LIVE_CITY 0.000000 LIVE_REGION_NOT_WORK_REGION 0.000000 REG_REGION_NOT_WORK_REGION 0.000000 REG_REGION_NOT_LIVE_REGION 0.000000 HOUR_APPR_PROCESS_START 0.000000 WEEKDAY_APPR_PROCESS_START 0.000000 REGION_RATING_CLIENT_W_CITY 0.000000 REGION_RATING_CLIENT 0.000000 FLAG_EMAIL 0.000000 FLAG_CONT_MOBILE 0.000000 ORGANIZATION_TYPE 0.000000 FLAG_WORK_PHONE 0.000000 FLAG_EMP_PHONE 0.000000 FLAG_MOBIL 0.000000 DAYS_ID_PUBLISH 0.000000 DAYS_REGISTRATION 0.000000 DAYS_EMPLOYED 0.000000 DAYS_BIRTH 0.000000 REGION_POPULATION_RELATIVE 0.000000 NAME_HOUSING_TYPE 0.000000 NAME_FAMILY_STATUS 0.000000 NAME_EDUCATION_TYPE 0.000000 NAME_INCOME_TYPE 0.000000 SK_ID_CURR 0.000000 dtype: float64
In [10]:
# removing the columns having more than 50% null values
app_df = application_df.loc[:, (application_df.isnull().sum()/len(application_df)*100) < 50]
app_df.head()
Out[10]:
| SK_ID_CURR | TARGET | NAME_CONTRACT_TYPE | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | NAME_TYPE_SUITE | NAME_INCOME_TYPE | NAME_EDUCATION_TYPE | NAME_FAMILY_STATUS | NAME_HOUSING_TYPE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | OCCUPATION_TYPE | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | WEEKDAY_APPR_PROCESS_START | HOUR_APPR_PROCESS_START | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | ORGANIZATION_TYPE | EXT_SOURCE_2 | EXT_SOURCE_3 | YEARS_BEGINEXPLUATATION_AVG | FLOORSMAX_AVG | YEARS_BEGINEXPLUATATION_MODE | FLOORSMAX_MODE | YEARS_BEGINEXPLUATATION_MEDI | FLOORSMAX_MEDI | TOTALAREA_MODE | EMERGENCYSTATE_MODE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 100002 | 1 | Cash loans | M | N | Y | 0 | 202500.0 | 406597.5 | 24700.5 | 351000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.018801 | -9461 | -637 | -3648.0 | -2120 | 1 | 1 | 0 | 1 | 1 | 0 | Laborers | 1.0 | 2 | 2 | WEDNESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.262949 | 0.139376 | 0.9722 | 0.0833 | 0.9722 | 0.0833 | 0.9722 | 0.0833 | 0.0149 | No | 2.0 | 2.0 | 2.0 | 2.0 | -1134.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 1 | 100003 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | 1129500.0 | Family | State servant | Higher education | Married | House / apartment | 0.003541 | -16765 | -1188 | -1186.0 | -291 | 1 | 1 | 0 | 1 | 1 | 0 | Core staff | 2.0 | 1 | 1 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 0.622246 | NaN | 0.9851 | 0.2917 | 0.9851 | 0.2917 | 0.9851 | 0.2917 | 0.0714 | No | 1.0 | 0.0 | 1.0 | 0.0 | -828.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2 | 100004 | 0 | Revolving loans | M | Y | Y | 0 | 67500.0 | 135000.0 | 6750.0 | 135000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.010032 | -19046 | -225 | -4260.0 | -2531 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 1.0 | 2 | 2 | MONDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.555912 | 0.729567 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -815.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3 | 100006 | 0 | Cash loans | F | N | Y | 0 | 135000.0 | 312682.5 | 29686.5 | 297000.0 | Unaccompanied | Working | Secondary / secondary special | Civil marriage | House / apartment | 0.008019 | -19005 | -3039 | -9833.0 | -2437 | 1 | 1 | 0 | 1 | 0 | 0 | Laborers | 2.0 | 2 | 2 | WEDNESDAY | 17 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.650442 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.0 | 2.0 | 0.0 | -617.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 100007 | 0 | Cash loans | M | N | Y | 0 | 121500.0 | 513000.0 | 21865.5 | 513000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.028663 | -19932 | -3038 | -4311.0 | -3458 | 1 | 1 | 0 | 1 | 0 | 0 | Core staff | 1.0 | 2 | 2 | THURSDAY | 11 | 0 | 0 | 0 | 0 | 1 | 1 | Religion | 0.322738 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1106.0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
In [11]:
app_df.shape
# now the number of columns have been reduced to 81 (41 columns have been removed)
Out[11]:
(307511, 81)
In [12]:
# checking the percentage of null values od the updated dataframe
(app_df.isnull().sum()/len(app_df)*100).sort_values(ascending=False)
Out[12]:
FLOORSMAX_AVG 49.760822 FLOORSMAX_MODE 49.760822 FLOORSMAX_MEDI 49.760822 YEARS_BEGINEXPLUATATION_AVG 48.781019 YEARS_BEGINEXPLUATATION_MODE 48.781019 YEARS_BEGINEXPLUATATION_MEDI 48.781019 TOTALAREA_MODE 48.268517 EMERGENCYSTATE_MODE 47.398304 OCCUPATION_TYPE 31.345545 EXT_SOURCE_3 19.825307 AMT_REQ_CREDIT_BUREAU_YEAR 13.501631 AMT_REQ_CREDIT_BUREAU_HOUR 13.501631 AMT_REQ_CREDIT_BUREAU_DAY 13.501631 AMT_REQ_CREDIT_BUREAU_WEEK 13.501631 AMT_REQ_CREDIT_BUREAU_MON 13.501631 AMT_REQ_CREDIT_BUREAU_QRT 13.501631 NAME_TYPE_SUITE 0.420148 DEF_30_CNT_SOCIAL_CIRCLE 0.332021 OBS_60_CNT_SOCIAL_CIRCLE 0.332021 DEF_60_CNT_SOCIAL_CIRCLE 0.332021 OBS_30_CNT_SOCIAL_CIRCLE 0.332021 EXT_SOURCE_2 0.214626 AMT_GOODS_PRICE 0.090403 AMT_ANNUITY 0.003902 CNT_FAM_MEMBERS 0.000650 DAYS_LAST_PHONE_CHANGE 0.000325 FLAG_OWN_CAR 0.000000 FLAG_OWN_REALTY 0.000000 NAME_CONTRACT_TYPE 0.000000 FLAG_DOCUMENT_2 0.000000 FLAG_DOCUMENT_3 0.000000 FLAG_DOCUMENT_4 0.000000 FLAG_DOCUMENT_5 0.000000 FLAG_DOCUMENT_6 0.000000 FLAG_DOCUMENT_7 0.000000 FLAG_DOCUMENT_8 0.000000 FLAG_DOCUMENT_9 0.000000 FLAG_DOCUMENT_10 0.000000 FLAG_DOCUMENT_11 0.000000 FLAG_DOCUMENT_12 0.000000 FLAG_DOCUMENT_13 0.000000 AMT_INCOME_TOTAL 0.000000 FLAG_DOCUMENT_15 0.000000 FLAG_DOCUMENT_16 0.000000 FLAG_DOCUMENT_17 0.000000 FLAG_DOCUMENT_18 0.000000 FLAG_DOCUMENT_19 0.000000 FLAG_DOCUMENT_20 0.000000 FLAG_DOCUMENT_21 0.000000 CNT_CHILDREN 0.000000 CODE_GENDER 0.000000 FLAG_DOCUMENT_14 0.000000 FLAG_EMP_PHONE 0.000000 AMT_CREDIT 0.000000 FLAG_WORK_PHONE 0.000000 FLAG_CONT_MOBILE 0.000000 FLAG_PHONE 0.000000 FLAG_EMAIL 0.000000 FLAG_MOBIL 0.000000 REGION_RATING_CLIENT 0.000000 REGION_RATING_CLIENT_W_CITY 0.000000 WEEKDAY_APPR_PROCESS_START 0.000000 HOUR_APPR_PROCESS_START 0.000000 REG_REGION_NOT_LIVE_REGION 0.000000 REG_REGION_NOT_WORK_REGION 0.000000 LIVE_REGION_NOT_WORK_REGION 0.000000 REG_CITY_NOT_LIVE_CITY 0.000000 REG_CITY_NOT_WORK_CITY 0.000000 LIVE_CITY_NOT_WORK_CITY 0.000000 ORGANIZATION_TYPE 0.000000 TARGET 0.000000 DAYS_ID_PUBLISH 0.000000 DAYS_REGISTRATION 0.000000 DAYS_EMPLOYED 0.000000 DAYS_BIRTH 0.000000 REGION_POPULATION_RELATIVE 0.000000 NAME_HOUSING_TYPE 0.000000 NAME_FAMILY_STATUS 0.000000 NAME_EDUCATION_TYPE 0.000000 NAME_INCOME_TYPE 0.000000 SK_ID_CURR 0.000000 dtype: float64
In [13]:
# getting columns with null values greater than 0% and less than or equal to 40%
app_df.columns[((app_df.isnull().sum()/len(app_df)*100) <= 40) & ((app_df.isnull().sum()/len(app_df)*100) > 0)]
# Now we are going to analyse these columns to find out the outliers
Out[13]:
Index(['AMT_ANNUITY', 'AMT_GOODS_PRICE', 'NAME_TYPE_SUITE', 'OCCUPATION_TYPE',
'CNT_FAM_MEMBERS', 'EXT_SOURCE_2', 'EXT_SOURCE_3',
'OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE',
'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE',
'DAYS_LAST_PHONE_CHANGE', 'AMT_REQ_CREDIT_BUREAU_HOUR',
'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK',
'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT',
'AMT_REQ_CREDIT_BUREAU_YEAR'],
dtype='object')
In [14]:
app_df[app_df['AMT_ANNUITY'].isnull()]
Out[14]:
| SK_ID_CURR | TARGET | NAME_CONTRACT_TYPE | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | NAME_TYPE_SUITE | NAME_INCOME_TYPE | NAME_EDUCATION_TYPE | NAME_FAMILY_STATUS | NAME_HOUSING_TYPE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | OCCUPATION_TYPE | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | WEEKDAY_APPR_PROCESS_START | HOUR_APPR_PROCESS_START | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | ORGANIZATION_TYPE | EXT_SOURCE_2 | EXT_SOURCE_3 | YEARS_BEGINEXPLUATATION_AVG | FLOORSMAX_AVG | YEARS_BEGINEXPLUATATION_MODE | FLOORSMAX_MODE | YEARS_BEGINEXPLUATATION_MEDI | FLOORSMAX_MEDI | TOTALAREA_MODE | EMERGENCYSTATE_MODE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 47531 | 155054 | 0 | Cash loans | M | N | N | 0 | 180000.0 | 450000.0 | NaN | 450000.0 | Unaccompanied | Commercial associate | Incomplete higher | Single / not married | House / apartment | 0.026392 | -10668 | -2523 | -4946.0 | -3238 | 1 | 1 | 1 | 1 | 1 | 0 | High skill tech staff | 1.0 | 2 | 2 | WEDNESDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.626896 | 0.372334 | 0.9662 | 0.0417 | 0.9662 | 0.0417 | 0.9662 | 0.0417 | 0.0090 | No | 1.0 | 0.0 | 1.0 | 0.0 | -2.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 |
| 50035 | 157917 | 0 | Cash loans | F | N | N | 0 | 94500.0 | 450000.0 | NaN | 450000.0 | Unaccompanied | Working | Lower secondary | Civil marriage | House / apartment | 0.035792 | -9027 | -1270 | -3640.0 | -741 | 1 | 1 | 1 | 1 | 0 | 0 | Laborers | 2.0 | 2 | 2 | MONDAY | 20 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 1 | 0.727274 | 0.468660 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -706.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 |
| 51594 | 159744 | 0 | Cash loans | F | N | N | 0 | 202500.0 | 539100.0 | NaN | 450000.0 | Unaccompanied | Working | Secondary / secondary special | Married | House / apartment | 0.046220 | -17376 | -3950 | -11524.0 | -831 | 1 | 1 | 0 | 1 | 0 | 0 | Cooking staff | 2.0 | 1 | 1 | WEDNESDAY | 15 | 0 | 0 | 0 | 1 | 1 | 1 | Self-employed | 0.738370 | 0.452534 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -199.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 |
| 55025 | 163757 | 0 | Cash loans | F | N | N | 0 | 162000.0 | 296280.0 | NaN | 225000.0 | Unaccompanied | State servant | Higher education | Married | House / apartment | 0.035792 | -11329 | -2040 | -3195.0 | -3069 | 1 | 1 | 0 | 1 | 0 | 0 | Core staff | 2.0 | 2 | 2 | FRIDAY | 13 | 0 | 0 | 0 | 1 | 1 | 1 | Government | 0.566316 | 0.220095 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -2841.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 4.0 |
| 59934 | 169487 | 0 | Cash loans | M | Y | N | 0 | 202500.0 | 360000.0 | NaN | 360000.0 | Unaccompanied | Working | Secondary / secondary special | Single / not married | House / apartment | 0.046220 | -19762 | -2498 | -11285.0 | -3305 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 1.0 | 1 | 1 | FRIDAY | 15 | 0 | 1 | 1 | 0 | 1 | 1 | Other | 0.583947 | 0.177704 | 0.9841 | 0.4583 | 0.9841 | 0.4583 | 0.9841 | 0.4583 | 0.0679 | No | 0.0 | 0.0 | 0.0 | 0.0 | -743.0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 6.0 |
| 75873 | 187985 | 0 | Cash loans | M | Y | N | 0 | 144000.0 | 219249.0 | NaN | 166500.0 | Unaccompanied | Working | Higher education | Single / not married | Rented apartment | 0.022800 | -20831 | -2450 | -771.0 | -4203 | 1 | 1 | 0 | 1 | 0 | 0 | Drivers | 1.0 | 2 | 2 | FRIDAY | 15 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.667564 | 0.425893 | 0.9831 | 0.1667 | 0.9831 | 0.1667 | 0.9831 | 0.1667 | 0.0687 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1986.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 11.0 |
| 89343 | 203726 | 0 | Cash loans | F | Y | N | 0 | 90000.0 | 157500.0 | NaN | 157500.0 | Unaccompanied | State servant | Secondary / secondary special | Married | House / apartment | 0.015221 | -12134 | -3721 | -858.0 | -591 | 1 | 1 | 0 | 1 | 1 | 0 | Medicine staff | 2.0 | 2 | 2 | WEDNESDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Medicine | 0.154811 | 0.297087 | 0.9781 | 0.1667 | 0.9782 | 0.1667 | 0.9781 | 0.1667 | 0.1218 | No | 0.0 | 0.0 | 0.0 | 0.0 | -348.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 |
| 123872 | 243648 | 0 | Cash loans | F | N | Y | 0 | 202500.0 | 929088.0 | NaN | 720000.0 | Unaccompanied | Working | Secondary / secondary special | Civil marriage | House / apartment | 0.019689 | -13902 | -3540 | -168.0 | -4250 | 1 | 1 | 0 | 1 | 0 | 1 | Secretaries | 2.0 | 2 | 2 | SATURDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.510934 | 0.581484 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1331.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 5.0 |
| 207186 | 340147 | 0 | Cash loans | M | N | N | 0 | 171000.0 | 486000.0 | NaN | 486000.0 | Unaccompanied | Commercial associate | Higher education | Married | House / apartment | 0.018634 | -10151 | -472 | -10127.0 | -2787 | 1 | 1 | 0 | 1 | 1 | 0 | Security staff | 2.0 | 2 | 2 | WEDNESDAY | 13 | 1 | 1 | 1 | 1 | 1 | 1 | Security | 0.706306 | 0.391055 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -295.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 2.0 |
| 227939 | 364022 | 0 | Cash loans | F | N | Y | 0 | 315000.0 | 628069.5 | NaN | 499500.0 | Unaccompanied | Commercial associate | Higher education | Married | Municipal apartment | 0.046220 | -16344 | -1478 | -482.0 | -4192 | 1 | 1 | 0 | 1 | 0 | 0 | Sales staff | 2.0 | 1 | 1 | WEDNESDAY | 11 | 0 | 1 | 1 | 0 | 0 | 0 | Business Entity Type 3 | 0.618698 | 0.431192 | 0.9866 | 0.3333 | 0.9866 | 0.3333 | 0.9866 | 0.3333 | 0.1613 | No | 1.0 | 0.0 | 1.0 | 0.0 | -1255.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 2.0 |
| 239329 | 377174 | 0 | Cash loans | F | N | Y | 0 | 157500.0 | 792000.0 | NaN | 792000.0 | Family | State servant | Secondary / secondary special | Married | House / apartment | 0.026392 | -17661 | -2885 | -8594.0 | -1221 | 1 | 1 | 0 | 1 | 1 | 0 | Managers | 2.0 | 2 | 2 | SATURDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Other | 0.656305 | 0.651260 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -322.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 |
| 241835 | 379997 | 0 | Cash loans | F | N | N | 0 | 315000.0 | 1483231.5 | NaN | 1354500.0 | Unaccompanied | Working | Higher education | Married | House / apartment | 0.072508 | -15072 | -152 | -7576.0 | -4389 | 1 | 1 | 1 | 1 | 1 | 0 | Accountants | 2.0 | 1 | 1 | WEDNESDAY | 15 | 0 | 1 | 1 | 0 | 1 | 1 | Self-employed | 0.267269 | NaN | 0.9806 | 0.5417 | 0.9796 | 0.5417 | 0.9806 | 0.5417 | 0.1860 | No | 0.0 | 0.0 | 0.0 | 0.0 | -504.0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
In [15]:
# checking for outliers using statistical summary of AMT_ANNUITY column and boxplot
print(app_df['AMT_ANNUITY'].describe(percentiles=[0.1,0.25,0.5,0.75,0.99]))
plt.figure(figsize=[14,7])
sns.boxplot(app_df['AMT_ANNUITY'])
count 307499.000000 mean 27108.573909 std 14493.737315 min 1615.500000 10% 11074.500000 25% 16524.000000 50% 24903.000000 75% 34596.000000 99% 70006.500000 max 258025.500000 Name: AMT_ANNUITY, dtype: float64
Out[15]:
<Axes: ylabel='AMT_ANNUITY'>
In [16]:
# From the above box plot shows that there are a huge number of outliers. Even the 99th percentile is also very less compared to the max value
# We can fill the null columns with median (24903.00)
In [17]:
# checking AMT_GOODS_PRICE column
app_df[app_df['AMT_GOODS_PRICE'].isnull()]
Out[17]:
| SK_ID_CURR | TARGET | NAME_CONTRACT_TYPE | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | NAME_TYPE_SUITE | NAME_INCOME_TYPE | NAME_EDUCATION_TYPE | NAME_FAMILY_STATUS | NAME_HOUSING_TYPE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | OCCUPATION_TYPE | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | WEEKDAY_APPR_PROCESS_START | HOUR_APPR_PROCESS_START | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | ORGANIZATION_TYPE | EXT_SOURCE_2 | EXT_SOURCE_3 | YEARS_BEGINEXPLUATATION_AVG | FLOORSMAX_AVG | YEARS_BEGINEXPLUATATION_MODE | FLOORSMAX_MODE | YEARS_BEGINEXPLUATATION_MEDI | FLOORSMAX_MEDI | TOTALAREA_MODE | EMERGENCYSTATE_MODE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 724 | 100837 | 0 | Revolving loans | F | N | Y | 2 | 45000.0 | 135000.0 | 6750.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Married | House / apartment | 0.024610 | -10072 | -381 | -519.0 | -1834 | 1 | 1 | 1 | 1 | 0 | 0 | Core staff | 4.0 | 2 | 2 | WEDNESDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Kindergarten | 0.378174 | NaN | 0.9791 | 0.0417 | 0.9791 | 0.0417 | 0.9791 | 0.0417 | 0.0079 | No | 2.0 | 1.0 | 2.0 | 1.0 | -2011.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 5937 | 106955 | 0 | Revolving loans | F | N | N | 0 | 157500.0 | 450000.0 | 22500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.010006 | -11993 | -2921 | -1289.0 | -1948 | 1 | 1 | 1 | 1 | 1 | 1 | Private service staff | 2.0 | 2 | 2 | SATURDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.464614 | 0.537070 | 0.9980 | 0.3333 | 0.9980 | 0.3333 | 0.9980 | 0.3333 | 0.1971 | No | 0.0 | 0.0 | 0.0 | 0.0 | -476.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 6425 | 107494 | 0 | Revolving loans | F | N | N | 0 | 67500.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Higher education | Married | House / apartment | 0.008474 | -9727 | -2712 | -4132.0 | -800 | 1 | 1 | 1 | 1 | 1 | 0 | Accountants | 2.0 | 2 | 2 | WEDNESDAY | 18 | 0 | 0 | 0 | 0 | 0 | 0 | Trade: type 7 | 0.565849 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 0.0 | -643.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 6703 | 107822 | 0 | Revolving loans | F | N | N | 1 | 121500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Civil marriage | House / apartment | 0.011657 | -11079 | -899 | -3765.0 | -572 | 1 | 1 | 1 | 1 | 1 | 0 | Managers | 3.0 | 1 | 1 | WEDNESDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.645519 | 0.863363 | 0.9767 | 0.1667 | 0.9767 | 0.1667 | 0.9767 | 0.1667 | 0.0452 | No | 1.0 | 0.0 | 1.0 | 0.0 | -1292.0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 7647 | 108913 | 0 | Revolving loans | M | N | Y | 0 | 180000.0 | 450000.0 | 22500.0 | NaN | NaN | Working | Higher education | Single / not married | House / apartment | 0.032561 | -9986 | -1847 | -4762.0 | -506 | 1 | 1 | 1 | 1 | 1 | 0 | Security staff | 1.0 | 1 | 1 | SATURDAY | 18 | 0 | 0 | 0 | 0 | 0 | 0 | Construction | 0.552557 | 0.661024 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -159.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 7880 | 109190 | 1 | Revolving loans | F | N | N | 0 | 121500.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Higher education | Single / not married | With parents | 0.006296 | -12390 | -640 | -6365.0 | -3597 | 1 | 1 | 0 | 1 | 1 | 0 | Managers | 1.0 | 3 | 3 | FRIDAY | 16 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.407925 | 0.240541 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.0 | 1.0 | 0.0 | -158.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 7995 | 109322 | 0 | Revolving loans | M | N | Y | 0 | 112500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.008230 | -12420 | -1610 | -6443.0 | -2463 | 1 | 1 | 0 | 1 | 1 | 0 | Security staff | 1.0 | 2 | 2 | THURSDAY | 16 | 0 | 1 | 1 | 1 | 1 | 1 | Security | 0.078597 | 0.504681 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 | 0.0 | 3.0 | 0.0 | -2.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 3.0 |
| 10819 | 112595 | 0 | Revolving loans | F | N | Y | 0 | 90000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.022800 | -18193 | -242 | -1462.0 | -1731 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 2.0 | 2 | 2 | SUNDAY | 15 | 0 | 0 | 0 | 0 | 0 | 0 | Transport: type 4 | 0.644126 | 0.553165 | 0.9970 | 0.5417 | 0.9970 | 0.5417 | 0.9970 | 0.5417 | 0.1257 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1952.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 11287 | 113148 | 0 | Revolving loans | F | N | Y | 4 | 225000.0 | 135000.0 | 6750.0 | NaN | NaN | State servant | Secondary / secondary special | Single / not married | House / apartment | 0.003541 | -17610 | -4662 | -8233.0 | -1148 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 5.0 | 1 | 1 | THURSDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | School | 0.419290 | 0.513694 | 0.9886 | 0.3750 | 0.9886 | 0.3750 | 0.9886 | 0.3750 | 0.4902 | No | 3.0 | 0.0 | 3.0 | 0.0 | -1780.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 |
| 13008 | 115162 | 0 | Revolving loans | F | N | Y | 1 | 157500.0 | 450000.0 | 22500.0 | NaN | NaN | State servant | Higher education | Married | House / apartment | 0.046220 | -14005 | -1404 | -2284.0 | -2282 | 1 | 1 | 1 | 1 | 1 | 1 | Laborers | 3.0 | 1 | 1 | TUESDAY | 14 | 0 | 0 | 0 | 0 | 1 | 1 | Housing | 0.719591 | 0.470456 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | -1837.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 2.0 |
| 14699 | 117150 | 0 | Revolving loans | M | N | N | 1 | 135000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.024610 | -9962 | -1676 | -4321.0 | -2592 | 1 | 1 | 1 | 1 | 1 | 0 | Drivers | 3.0 | 2 | 2 | THURSDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Military | 0.358644 | 0.465069 | 0.9846 | 0.1667 | 0.9846 | 0.1667 | 0.9846 | 0.1667 | 0.0757 | No | 6.0 | 0.0 | 6.0 | 0.0 | -378.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 2.0 |
| 15953 | 118618 | 0 | Revolving loans | F | N | N | 1 | 90000.0 | 270000.0 | 13500.0 | NaN | NaN | Commercial associate | Higher education | Married | House / apartment | 0.008625 | -10233 | -2216 | -4304.0 | -2191 | 1 | 1 | 1 | 1 | 1 | 0 | Waiters/barmen staff | 3.0 | 2 | 2 | WEDNESDAY | 15 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.761872 | 0.617826 | 0.9841 | 0.3333 | 0.9841 | 0.3333 | 0.9841 | 0.3333 | 0.1372 | No | 2.0 | 0.0 | 2.0 | 0.0 | -1604.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 18935 | 122080 | 0 | Revolving loans | M | N | Y | 0 | 157500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Lower secondary | Married | House / apartment | 0.003813 | -9727 | -2137 | -4394.0 | -2301 | 1 | 1 | 0 | 1 | 1 | 0 | Sales staff | 2.0 | 2 | 2 | MONDAY | 7 | 0 | 0 | 0 | 0 | 1 | 1 | Self-employed | 0.283778 | NaN | 0.9816 | 0.1667 | 0.9816 | 0.1667 | 0.9816 | 0.1667 | 0.2272 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1528.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 19178 | 122374 | 0 | Revolving loans | F | N | Y | 0 | 67500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.006008 | -10777 | -3330 | -706.0 | -873 | 1 | 1 | 1 | 1 | 0 | 0 | Laborers | 2.0 | 2 | 2 | TUESDAY | 15 | 0 | 0 | 0 | 0 | 1 | 1 | Business Entity Type 2 | 0.600496 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -473.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 19921 | 123233 | 0 | Revolving loans | F | N | N | 0 | 90000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.004960 | -12920 | -100 | -5986.0 | -4363 | 1 | 1 | 0 | 1 | 1 | 0 | Security staff | 1.0 | 2 | 2 | SATURDAY | 18 | 0 | 0 | 0 | 0 | 0 | 0 | Security | 0.603896 | 0.374021 | 0.9851 | 0.1667 | 0.9856 | 0.1667 | 0.9856 | 0.1667 | 0.0292 | No | 2.0 | 0.0 | 2.0 | 0.0 | -564.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 2.0 | 1.0 |
| 21193 | 124697 | 0 | Revolving loans | F | N | Y | 0 | 202500.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Higher education | Married | Co-op apartment | 0.025164 | -16944 | -686 | -9883.0 | -471 | 1 | 1 | 0 | 1 | 1 | 1 | Laborers | 2.0 | 2 | 2 | THURSDAY | 15 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 2 | 0.313315 | 0.510090 | 0.9742 | 0.1667 | 0.9742 | 0.1667 | 0.9742 | 0.1667 | 0.0559 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1822.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 21338 | 124859 | 0 | Revolving loans | M | N | Y | 0 | 225000.0 | 675000.0 | 33750.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.035792 | -17658 | -5986 | -12170.0 | -1212 | 1 | 1 | 1 | 1 | 0 | 0 | Managers | 2.0 | 2 | 2 | TUESDAY | 16 | 0 | 0 | 0 | 0 | 0 | 0 | Other | 0.537859 | 0.255332 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1455.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 21546 | 125096 | 0 | Revolving loans | F | N | N | 1 | 45000.0 | 135000.0 | 6750.0 | NaN | NaN | Working | Secondary / secondary special | Civil marriage | Municipal apartment | 0.031329 | -7727 | -1159 | -7704.0 | -381 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 3.0 | 2 | 2 | FRIDAY | 12 | 0 | 0 | 0 | 0 | 1 | 1 | Government | 0.491223 | NaN | 0.9866 | 0.0417 | 0.9866 | 0.0417 | 0.9866 | 0.0417 | 0.0133 | No | 15.0 | 0.0 | 15.0 | 0.0 | -245.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 25391 | 129532 | 0 | Revolving loans | M | N | Y | 1 | 382500.0 | 270000.0 | 13500.0 | NaN | NaN | Commercial associate | Higher education | Single / not married | House / apartment | 0.046220 | -8993 | -641 | -2332.0 | -1676 | 1 | 1 | 1 | 1 | 1 | 1 | Managers | 2.0 | 1 | 1 | SATURDAY | 16 | 0 | 1 | 1 | 0 | 1 | 1 | Construction | 0.633927 | 0.051329 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 | 0.0 | 3.0 | 0.0 | -208.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 26398 | 130692 | 0 | Revolving loans | M | N | N | 0 | 135000.0 | 495000.0 | 24750.0 | NaN | NaN | Pensioner | Higher education | Single / not married | House / apartment | 0.020713 | -22009 | 365243 | -342.0 | -1397 | 1 | 0 | 0 | 1 | 1 | 0 | NaN | 1.0 | 3 | 2 | FRIDAY | 6 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.005691 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -462.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 26736 | 131077 | 0 | Revolving loans | F | N | Y | 0 | 90000.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.018634 | -14069 | -2646 | -2821.0 | -1141 | 1 | 1 | 0 | 1 | 0 | 0 | Managers | 2.0 | 2 | 2 | THURSDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Postal | 0.592876 | 0.450747 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -1030.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 27003 | 131385 | 0 | Revolving loans | M | N | Y | 0 | 180000.0 | 450000.0 | 22500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.022625 | -16559 | -496 | -3284.0 | -111 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 2.0 | 2 | 2 | WEDNESDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Other | 0.561724 | 0.258084 | 0.9747 | 0.1667 | 0.9747 | 0.1667 | 0.9747 | 0.1667 | 0.0439 | No | 0.0 | 0.0 | 0.0 | 0.0 | -813.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 2.0 |
| 28201 | 132778 | 0 | Revolving loans | F | N | Y | 0 | 45000.0 | 315000.0 | 15750.0 | NaN | NaN | Pensioner | Secondary / secondary special | Married | House / apartment | 0.019101 | -23588 | 365243 | -3752.0 | -4715 | 1 | 0 | 0 | 1 | 1 | 0 | NaN | 2.0 | 2 | 2 | TUESDAY | 17 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.642666 | 0.339288 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -1646.0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 29059 | 133761 | 0 | Revolving loans | F | N | Y | 2 | 81000.0 | 247500.0 | 12375.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.031329 | -11751 | -2601 | -975.0 | -4392 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 4.0 | 2 | 2 | THURSDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Industry: type 3 | 0.640474 | 0.508287 | 0.9816 | 0.1667 | 0.9816 | 0.1667 | 0.9816 | 0.1667 | 0.0776 | No | 0.0 | 0.0 | 0.0 | 0.0 | -2202.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 30164 | 135010 | 0 | Revolving loans | F | N | Y | 1 | 121500.0 | 405000.0 | 20250.0 | NaN | NaN | Commercial associate | Higher education | Married | House / apartment | 0.026392 | -10168 | -759 | -4235.0 | -2840 | 1 | 1 | 1 | 1 | 0 | 0 | Core staff | 3.0 | 2 | 2 | SATURDAY | 20 | 0 | 0 | 0 | 0 | 0 | 0 | School | 0.709507 | 0.798137 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1698.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 30294 | 135159 | 0 | Revolving loans | M | N | Y | 0 | 225000.0 | 450000.0 | 22500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.022625 | -12943 | -2214 | -7000.0 | -4355 | 1 | 1 | 0 | 1 | 0 | 0 | Laborers | 2.0 | 2 | 2 | WEDNESDAY | 17 | 0 | 0 | 0 | 0 | 1 | 1 | Transport: type 4 | 0.683045 | 0.689479 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1525.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 32143 | 137268 | 0 | Revolving loans | M | N | Y | 0 | 112500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.005002 | -14063 | -1593 | -6647.0 | -4723 | 1 | 1 | 1 | 1 | 1 | 0 | Drivers | 2.0 | 3 | 3 | MONDAY | 11 | 0 | 0 | 0 | 1 | 0 | 1 | Business Entity Type 3 | 0.598895 | 0.665855 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -2576.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 36250 | 141975 | 0 | Revolving loans | F | N | Y | 0 | 90000.0 | 202500.0 | 10125.0 | NaN | NaN | Pensioner | Secondary / secondary special | Widow | House / apartment | 0.011657 | -20277 | 365243 | -11951.0 | -2809 | 1 | 0 | 0 | 1 | 1 | 0 | NaN | 1.0 | 1 | 1 | SATURDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.666481 | NaN | 0.9617 | 0.1250 | 0.9618 | 0.1250 | 0.9617 | 0.1250 | 0.0385 | No | 1.0 | 0.0 | 1.0 | 0.0 | -129.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 36721 | 142532 | 0 | Revolving loans | M | N | Y | 1 | 202500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.007114 | -13530 | -1654 | -299.0 | -4380 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 3.0 | 2 | 2 | WEDNESDAY | 16 | 0 | 0 | 0 | 0 | 1 | 1 | Construction | 0.713971 | 0.340906 | 0.9851 | 0.1667 | 0.9851 | 0.1667 | 0.9851 | 0.1667 | 0.0760 | No | 1.0 | 0.0 | 1.0 | 0.0 | -1511.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 39230 | 145435 | 0 | Revolving loans | F | N | Y | 0 | 225000.0 | 382500.0 | 19125.0 | NaN | NaN | Working | Higher education | Married | House / apartment | 0.010006 | -17922 | -1411 | -652.0 | -1445 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 2.0 | 2 | 2 | FRIDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | School | 0.660976 | 0.306202 | 0.9871 | 0.0833 | 0.9871 | 0.0833 | 0.9871 | 0.0833 | 0.0161 | No | 0.0 | 0.0 | 0.0 | 0.0 | -598.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 40571 | 146988 | 0 | Revolving loans | F | N | Y | 0 | 202500.0 | 1350000.0 | 67500.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Married | House / apartment | 0.030755 | -17986 | -4678 | -4504.0 | -1532 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 2.0 | 2 | 2 | MONDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.638104 | NaN | 0.9851 | 0.3333 | 0.9851 | 0.3333 | 0.9851 | 0.3333 | 0.1506 | No | 0.0 | 0.0 | 0.0 | 0.0 | -569.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 41099 | 147593 | 1 | Revolving loans | F | N | N | 0 | 58500.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Married | Rented apartment | 0.009334 | -15092 | -623 | -7436.0 | -4505 | 1 | 1 | 1 | 1 | 0 | 0 | Sales staff | 2.0 | 2 | 2 | SATURDAY | 10 | 0 | 0 | 0 | 1 | 1 | 0 | Self-employed | 0.559127 | 0.401407 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -387.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 41161 | 147655 | 0 | Revolving loans | M | N | Y | 1 | 112500.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.008625 | -10766 | -2275 | -7975.0 | -3402 | 1 | 1 | 0 | 1 | 1 | 0 | Core staff | 3.0 | 2 | 2 | FRIDAY | 11 | 0 | 0 | 0 | 0 | 1 | 1 | Other | 0.512021 | 0.819318 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1781.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 41982 | 148605 | 0 | Revolving loans | M | N | Y | 0 | 450000.0 | 675000.0 | 33750.0 | NaN | NaN | Commercial associate | Lower secondary | Unknown | Municipal apartment | 0.015221 | -12396 | -1161 | -3265.0 | -4489 | 1 | 1 | 1 | 1 | 1 | 0 | Managers | NaN | 2 | 2 | THURSDAY | 15 | 0 | 1 | 1 | 0 | 1 | 1 | Insurance | 0.700618 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 | 0.0 | 3.0 | 0.0 | -876.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 42024 | 148658 | 0 | Revolving loans | F | N | Y | 0 | 67500.0 | 135000.0 | 6750.0 | NaN | NaN | Working | Secondary / secondary special | Civil marriage | House / apartment | 0.009175 | -18258 | -1024 | -1795.0 | -1805 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 2.0 | 2 | 2 | THURSDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Industry: type 13 | 0.525216 | 0.457900 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -759.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 43630 | 150507 | 0 | Revolving loans | M | N | Y | 0 | 306000.0 | 1350000.0 | 67500.0 | NaN | NaN | Working | Higher education | Civil marriage | House / apartment | 0.007330 | -13197 | -2062 | -619.0 | -4038 | 1 | 1 | 1 | 1 | 0 | 0 | Managers | 2.0 | 2 | 2 | MONDAY | 17 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.697526 | 0.399676 | 0.9960 | 0.3750 | 0.9960 | 0.3750 | 0.9960 | 0.3750 | 0.2435 | No | 1.0 | 0.0 | 1.0 | 0.0 | -306.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 45657 | 152889 | 0 | Revolving loans | M | N | Y | 2 | 112500.0 | 135000.0 | 6750.0 | NaN | NaN | State servant | Secondary / secondary special | Married | House / apartment | 0.016612 | -13716 | -1466 | -8600.0 | -4045 | 1 | 1 | 1 | 1 | 0 | 0 | Laborers | 4.0 | 2 | 2 | MONDAY | 12 | 0 | 0 | 0 | 0 | 1 | 1 | School | 0.160135 | 0.617826 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -577.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 47627 | 155169 | 0 | Revolving loans | F | N | Y | 0 | 63000.0 | 135000.0 | 6750.0 | NaN | NaN | Pensioner | Secondary / secondary special | Married | House / apartment | 0.031329 | -21555 | 365243 | -10745.0 | -3935 | 1 | 0 | 0 | 1 | 1 | 0 | NaN | 2.0 | 2 | 2 | WEDNESDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.652740 | 0.754406 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1245.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 50540 | 158525 | 1 | Revolving loans | F | N | Y | 0 | 90000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.031329 | -8160 | -279 | -5946.0 | -224 | 1 | 1 | 0 | 1 | 1 | 0 | NaN | 2.0 | 2 | 2 | FRIDAY | 15 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.190088 | 0.285180 | 0.9881 | 0.1667 | 0.9881 | 0.1667 | 0.9881 | 0.1667 | 0.0883 | No | 6.0 | 2.0 | 5.0 | 1.0 | -194.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 52711 | 161052 | 0 | Revolving loans | M | N | Y | 1 | 135000.0 | 270000.0 | 13500.0 | NaN | NaN | Commercial associate | Higher education | Married | House / apartment | 0.046220 | -13482 | -2405 | -3893.0 | -3559 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 3.0 | 1 | 1 | SATURDAY | 13 | 0 | 1 | 1 | 0 | 1 | 1 | Government | 0.703034 | 0.729567 | 0.9866 | 0.3333 | 0.9866 | 0.3333 | 0.9866 | 0.3333 | 0.0608 | No | 1.0 | 0.0 | 0.0 | 0.0 | -898.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 52955 | 161332 | 0 | Revolving loans | F | N | Y | 0 | 126000.0 | 337500.0 | 16875.0 | NaN | NaN | Working | Higher education | Married | House / apartment | 0.008625 | -19614 | -4330 | -9709.0 | -2879 | 1 | 1 | 1 | 1 | 0 | 0 | Managers | 2.0 | 2 | 2 | SATURDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.654344 | 0.591977 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -716.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 |
| 54045 | 162615 | 0 | Revolving loans | F | N | Y | 0 | 135000.0 | 270000.0 | 13500.0 | NaN | NaN | Pensioner | Secondary / secondary special | Separated | House / apartment | 0.002042 | -22392 | 365243 | -548.0 | -2153 | 1 | 0 | 0 | 1 | 1 | 0 | NaN | 1.0 | 3 | 3 | WEDNESDAY | 14 | 0 | 0 | 0 | 1 | 0 | 0 | XNA | 0.602132 | 0.554947 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -494.0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 7.0 |
| 55600 | 164423 | 0 | Revolving loans | F | N | Y | 0 | 81000.0 | 202500.0 | 10125.0 | NaN | NaN | Pensioner | Secondary / secondary special | Married | House / apartment | 0.009630 | -22061 | 365243 | -4961.0 | -4393 | 1 | 0 | 0 | 1 | 1 | 0 | NaN | 2.0 | 2 | 2 | TUESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.465713 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -371.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 56002 | 164897 | 1 | Revolving loans | F | N | Y | 0 | 180000.0 | 855000.0 | 42750.0 | NaN | NaN | Commercial associate | Higher education | Married | House / apartment | 0.007020 | -20278 | -2357 | -10157.0 | -3195 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 2.0 | 2 | 2 | FRIDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.535939 | 0.124519 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1291.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 58595 | 167920 | 0 | Revolving loans | F | N | Y | 0 | 88650.0 | 247500.0 | 12375.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Widow | Municipal apartment | 0.003818 | -17812 | -4098 | -6219.0 | -1339 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 1.0 | 2 | 2 | FRIDAY | 8 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.143668 | NaN | 0.9861 | 0.1667 | 0.9861 | 0.1667 | 0.9861 | 0.1667 | 0.0742 | No | 0.0 | 0.0 | 0.0 | 0.0 | -523.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 58653 | 167989 | 0 | Revolving loans | M | N | Y | 0 | 157500.0 | 180000.0 | 9000.0 | NaN | NaN | Commercial associate | Higher education | Single / not married | House / apartment | 0.046220 | -10058 | -1534 | -3988.0 | -2720 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 1.0 | 1 | 1 | MONDAY | 20 | 0 | 0 | 0 | 0 | 1 | 1 | Trade: type 2 | 0.177997 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 8.0 | 0.0 | 8.0 | 0.0 | -855.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 |
| 60622 | 170291 | 0 | Revolving loans | M | N | Y | 0 | 135000.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.018029 | -13459 | -958 | -6473.0 | -4135 | 1 | 1 | 0 | 1 | 1 | 0 | Laborers | 2.0 | 3 | 2 | MONDAY | 4 | 0 | 0 | 0 | 0 | 0 | 0 | Trade: type 2 | 0.316400 | 0.728141 | 0.9771 | 0.1667 | 0.9772 | 0.1667 | 0.9771 | 0.1667 | 0.0770 | No | 2.0 | 0.0 | 2.0 | 0.0 | -1850.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 61022 | 170764 | 0 | Revolving loans | F | N | N | 1 | 112500.0 | 202500.0 | 10125.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Single / not married | With parents | 0.018850 | -11412 | -1147 | -9431.0 | -3379 | 1 | 1 | 1 | 1 | 1 | 1 | Sales staff | 2.0 | 2 | 2 | THURSDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.712701 | 0.526295 | 0.9871 | 0.3333 | 0.9871 | 0.3333 | 0.9871 | 0.3333 | 0.2007 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1789.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 61072 | 170823 | 0 | Revolving loans | F | N | Y | 0 | 67500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.009334 | -12356 | -5016 | -3787.0 | -4691 | 1 | 1 | 0 | 1 | 0 | 0 | Laborers | 2.0 | 2 | 2 | FRIDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | Medicine | 0.690463 | 0.399676 | 0.9727 | 0.0000 | 0.9727 | 0.0000 | 0.9727 | 0.0000 | 0.0016 | No | 0.0 | 0.0 | 0.0 | 0.0 | -557.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 61618 | 171462 | 0 | Revolving loans | M | N | Y | 0 | 180000.0 | 180000.0 | 9000.0 | NaN | NaN | State servant | Higher education | Single / not married | House / apartment | 0.019689 | -10286 | -3195 | -463.0 | -2794 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 1.0 | 2 | 2 | FRIDAY | 8 | 0 | 0 | 0 | 0 | 0 | 0 | Police | 0.433092 | 0.634706 | 0.9727 | 0.0833 | 0.9727 | 0.0833 | 0.9727 | 0.0833 | 0.0212 | No | 0.0 | 0.0 | 0.0 | 0.0 | -2780.0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 63319 | 173434 | 0 | Revolving loans | F | N | Y | 0 | 292500.0 | 675000.0 | 33750.0 | NaN | NaN | Commercial associate | Higher education | Married | House / apartment | 0.031329 | -13177 | -1620 | -7133.0 | -4621 | 1 | 1 | 0 | 1 | 1 | 0 | Managers | 2.0 | 2 | 2 | TUESDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.720504 | 0.000527 | 0.9881 | 0.1667 | 0.9881 | 0.1667 | 0.9881 | 0.1667 | 0.0803 | No | 4.0 | 0.0 | 4.0 | 0.0 | -161.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 64118 | 174350 | 0 | Revolving loans | F | N | Y | 0 | 72000.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.014520 | -14509 | -394 | -3139.0 | -4429 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 1.0 | 2 | 2 | SATURDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Kindergarten | 0.501123 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -191.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 64184 | 174427 | 0 | Revolving loans | F | N | Y | 1 | 90000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.016612 | -10311 | -840 | -196.0 | -566 | 1 | 1 | 1 | 1 | 0 | 0 | Sales staff | 3.0 | 2 | 2 | TUESDAY | 18 | 0 | 0 | 0 | 0 | 1 | 1 | Self-employed | 0.601838 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | -469.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 66168 | 176745 | 0 | Revolving loans | M | N | Y | 0 | 135000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.018634 | -17389 | -998 | -420.0 | -924 | 1 | 1 | 1 | 1 | 0 | 0 | Drivers | 2.0 | 2 | 2 | MONDAY | 15 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.775600 | NaN | 0.9836 | 0.3333 | 0.9836 | 0.3333 | 0.9836 | 0.3333 | 0.1905 | No | 13.0 | 0.0 | 13.0 | 0.0 | -1234.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 67609 | 178403 | 0 | Revolving loans | M | N | Y | 2 | 45000.0 | 135000.0 | 6750.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Married | House / apartment | 0.006008 | -14536 | -4265 | -7377.0 | -4267 | 1 | 1 | 0 | 1 | 0 | 0 | Laborers | 4.0 | 2 | 2 | TUESDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Housing | 0.584558 | 0.556727 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -596.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 69461 | 180561 | 1 | Revolving loans | F | N | Y | 1 | 103500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.010147 | -9997 | -1308 | -9046.0 | -1873 | 1 | 1 | 1 | 1 | 0 | 0 | Laborers | 2.0 | 2 | 2 | SATURDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.544694 | 0.684828 | 0.9767 | 0.1667 | 0.9767 | 0.1667 | 0.9767 | 0.1667 | 0.0309 | No | 7.0 | 0.0 | 7.0 | 0.0 | -1940.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 69525 | 180635 | 0 | Revolving loans | F | N | Y | 2 | 49500.0 | 135000.0 | 6750.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.018209 | -12582 | -795 | -472.0 | -3249 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 4.0 | 3 | 3 | THURSDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Medicine | 0.079540 | 0.519097 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | -264.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 70661 | 181966 | 0 | Revolving loans | F | N | Y | 0 | 112500.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.019689 | -12920 | -3139 | -5291.0 | -1033 | 1 | 1 | 1 | 1 | 1 | 0 | Managers | 2.0 | 2 | 2 | WEDNESDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.592718 | 0.709189 | 0.9757 | 0.1667 | 0.9757 | 0.1667 | 0.9757 | 0.1667 | 0.0613 | No | 4.0 | 1.0 | 4.0 | 1.0 | -520.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 70731 | 182043 | 0 | Revolving loans | F | N | Y | 0 | 202500.0 | 157500.0 | 7875.0 | NaN | NaN | Working | Higher education | Married | With parents | 0.008230 | -10152 | -1813 | -441.0 | -2752 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 2.0 | 2 | 2 | SATURDAY | 17 | 0 | 0 | 0 | 0 | 0 | 0 | Bank | 0.393569 | 0.472253 | 0.9866 | 0.4167 | 0.9866 | 0.4167 | 0.9866 | 0.4167 | 0.0698 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1659.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 |
| 72166 | 183693 | 0 | Revolving loans | F | N | Y | 1 | 135000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.031329 | -14492 | -6953 | -4215.0 | -4213 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 3.0 | 2 | 2 | THURSDAY | 7 | 0 | 0 | 0 | 0 | 0 | 0 | Kindergarten | 0.515528 | 0.526295 | 0.9811 | 0.1667 | 0.9811 | 0.1667 | 0.9811 | 0.1667 | 0.0534 | No | 2.0 | 0.0 | 2.0 | 0.0 | -1708.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 72902 | 184538 | 0 | Revolving loans | F | N | Y | 0 | 81000.0 | 225000.0 | 11250.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.018209 | -13342 | -2166 | -3432.0 | -5792 | 1 | 1 | 1 | 1 | 0 | 0 | Core staff | 2.0 | 3 | 3 | THURSDAY | 12 | 0 | 0 | 0 | 0 | 1 | 1 | Agriculture | 0.534846 | 0.622922 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | -525.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.0 |
| 74676 | 186598 | 0 | Revolving loans | M | N | Y | 0 | 180000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.072508 | -8268 | -885 | -404.0 | -944 | 1 | 1 | 1 | 1 | 1 | 1 | Core staff | 1.0 | 1 | 1 | TUESDAY | 16 | 0 | 0 | 0 | 0 | 0 | 0 | Bank | 0.446474 | 0.410103 | 0.9757 | 0.3333 | 0.9757 | 0.3333 | 0.9757 | 0.3333 | 0.1373 | No | 0.0 | 0.0 | 0.0 | 0.0 | -301.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 75036 | 187023 | 0 | Revolving loans | F | N | Y | 0 | 157500.0 | 675000.0 | 33750.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Married | House / apartment | 0.024610 | -10145 | -1686 | -2046.0 | -2523 | 1 | 1 | 1 | 1 | 0 | 0 | NaN | 2.0 | 2 | 2 | FRIDAY | 12 | 0 | 0 | 0 | 1 | 1 | 0 | Government | 0.686801 | 0.218859 | 0.9831 | 0.0417 | 0.9831 | 0.0417 | 0.9831 | 0.0417 | 0.0088 | No | 5.0 | 0.0 | 5.0 | 0.0 | -1324.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 6.0 |
| 78786 | 191335 | 1 | Revolving loans | F | N | Y | 2 | 67500.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.007120 | -16363 | -953 | -9646.0 | -4144 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 4.0 | 2 | 2 | FRIDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Postal | 0.196660 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -399.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 79108 | 191700 | 0 | Revolving loans | F | N | Y | 1 | 103500.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Civil marriage | House / apartment | 0.031329 | -16145 | -4327 | -4463.0 | -3918 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 3.0 | 2 | 2 | SATURDAY | 12 | 0 | 0 | 0 | 0 | 1 | 1 | Business Entity Type 3 | 0.688797 | 0.206779 | 0.9781 | 0.0417 | 0.9782 | 0.0417 | 0.9781 | 0.0417 | 0.0127 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1152.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 79484 | 192140 | 0 | Revolving loans | M | N | Y | 0 | 225000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.006629 | -17904 | -1230 | -1016.0 | -1393 | 1 | 1 | 1 | 1 | 1 | 0 | Drivers | 2.0 | 2 | 2 | WEDNESDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.610519 | 0.684828 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -251.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 80669 | 193529 | 0 | Revolving loans | F | N | Y | 0 | 180000.0 | 405000.0 | 20250.0 | NaN | NaN | Working | Higher education | Married | House / apartment | 0.024610 | -20232 | -2389 | -6752.0 | -3697 | 1 | 1 | 1 | 1 | 1 | 0 | High skill tech staff | 2.0 | 2 | 2 | SATURDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.634754 | 0.746300 | 0.9727 | 0.0417 | 0.9727 | 0.0417 | 0.9727 | 0.0417 | 0.0091 | No | 0.0 | 0.0 | 0.0 | 0.0 | -570.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 83784 | 197173 | 0 | Revolving loans | F | N | N | 1 | 76500.0 | 247500.0 | 12375.0 | NaN | NaN | State servant | Secondary / secondary special | Married | House / apartment | 0.035792 | -8398 | -1836 | -2937.0 | -627 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 3.0 | 2 | 2 | SATURDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.645433 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | -1754.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 85172 | 198819 | 0 | Revolving loans | F | N | Y | 1 | 139500.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.010147 | -13244 | -109 | -4947.0 | -4958 | 1 | 1 | 1 | 1 | 0 | 0 | NaN | 3.0 | 2 | 2 | SATURDAY | 11 | 0 | 0 | 0 | 0 | 1 | 1 | Business Entity Type 3 | 0.217530 | 0.474051 | 0.9876 | 0.0833 | 0.9876 | 0.0833 | 0.9876 | 0.0833 | 0.0245 | No | 1.0 | 1.0 | 1.0 | 1.0 | -653.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 85861 | 199632 | 0 | Revolving loans | F | N | Y | 0 | 90000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Widow | House / apartment | 0.020713 | -22970 | -591 | -7789.0 | -4441 | 1 | 1 | 1 | 1 | 0 | 0 | Cooking staff | 1.0 | 3 | 3 | MONDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | Medicine | 0.195247 | 0.448962 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 1.0 | 2.0 | 1.0 | -222.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 86000 | 199789 | 1 | Revolving loans | F | N | Y | 0 | 40500.0 | 135000.0 | 6750.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.018209 | -17975 | -1741 | -2200.0 | -1514 | 1 | 1 | 1 | 1 | 0 | 0 | NaN | 2.0 | 3 | 3 | FRIDAY | 14 | 0 | 0 | 0 | 0 | 1 | 1 | Housing | 0.304413 | 0.288130 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -980.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 3.0 |
| 86005 | 199794 | 1 | Revolving loans | F | N | Y | 0 | 112500.0 | 247500.0 | 12375.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.015221 | -18019 | -3174 | -2579.0 | -1230 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 2.0 | 2 | 2 | SATURDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.546527 | 0.152866 | 0.9781 | 0.1667 | 0.9782 | 0.1667 | 0.9781 | 0.1667 | 0.0656 | No | 0.0 | 0.0 | 0.0 | 0.0 | -2296.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 |
| 87095 | 201085 | 0 | Revolving loans | F | N | Y | 1 | 67500.0 | 135000.0 | 6750.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Single / not married | House / apartment | 0.009175 | -14808 | -3223 | -8184.0 | -4974 | 1 | 1 | 1 | 1 | 1 | 0 | Medicine staff | 2.0 | 2 | 2 | SUNDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Other | 0.746619 | 0.661024 | 0.9836 | 0.1667 | 0.9801 | 0.1667 | 0.9836 | 0.1667 | 0.0699 | No | 0.0 | 0.0 | 0.0 | 0.0 | -737.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 87144 | 201141 | 0 | Revolving loans | F | N | Y | 0 | 81000.0 | 202500.0 | 10125.0 | NaN | NaN | State servant | Secondary / secondary special | Civil marriage | House / apartment | 0.025164 | -15392 | -4080 | -4111.0 | -5372 | 1 | 1 | 1 | 1 | 1 | 0 | Medicine staff | 2.0 | 2 | 2 | THURSDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Medicine | 0.674683 | 0.569149 | 0.9811 | 0.0417 | 0.9811 | 0.0417 | 0.9811 | 0.0417 | 0.0122 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1630.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 87226 | 201232 | 0 | Revolving loans | F | N | Y | 0 | 180000.0 | 337500.0 | 16875.0 | NaN | NaN | Working | Incomplete higher | Married | House / apartment | 0.006671 | -13694 | -4360 | -8933.0 | -702 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 2.0 | 2 | 2 | SATURDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.600087 | 0.142873 | 0.9876 | 0.0417 | 0.9876 | 0.0417 | 0.9876 | 0.0417 | 0.0065 | No | 1.0 | 0.0 | 1.0 | 0.0 | -364.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 88544 | 202806 | 0 | Revolving loans | F | N | Y | 0 | 112500.0 | 405000.0 | 20250.0 | NaN | NaN | Working | Higher education | Civil marriage | House / apartment | 0.035792 | -13589 | -359 | -7530.0 | -3962 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 2.0 | 2 | 2 | TUESDAY | 17 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.563613 | 0.610991 | 0.9831 | 0.3333 | 0.9831 | 0.3333 | 0.9831 | 0.3333 | 0.1565 | No | 0.0 | 0.0 | 0.0 | 0.0 | -583.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 89085 | 203438 | 0 | Revolving loans | F | N | Y | 1 | 157500.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.009334 | -16853 | -1471 | -3417.0 | -370 | 1 | 1 | 1 | 1 | 0 | 0 | Accountants | 2.0 | 2 | 2 | TUESDAY | 12 | 0 | 0 | 0 | 0 | 1 | 1 | Self-employed | 0.633861 | 0.221335 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -237.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 89698 | 204141 | 0 | Revolving loans | F | N | Y | 0 | 67500.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Higher education | Single / not married | House / apartment | 0.010966 | -12793 | -1496 | -403.0 | -716 | 1 | 1 | 0 | 1 | 0 | 0 | Core staff | 1.0 | 2 | 2 | FRIDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Kindergarten | 0.560975 | 0.524496 | 0.9990 | 0.1667 | 0.9990 | 0.1667 | 0.9990 | 0.1667 | 0.0580 | No | 1.0 | 0.0 | 1.0 | 0.0 | -217.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 |
| 90898 | 205537 | 0 | Revolving loans | M | N | Y | 0 | 81000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.010276 | -15505 | -4231 | -8122.0 | -4169 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 2.0 | 2 | 2 | FRIDAY | 16 | 0 | 0 | 0 | 0 | 1 | 1 | Self-employed | 0.655027 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -197.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 96192 | 211675 | 0 | Revolving loans | F | N | Y | 3 | 90000.0 | 135000.0 | 6750.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.016612 | -12130 | -1579 | -5245.0 | -4074 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 5.0 | 2 | 2 | WEDNESDAY | 8 | 0 | 0 | 0 | 0 | 0 | 0 | Other | 0.542361 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.0 | 2.0 | 0.0 | -199.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 97022 | 212618 | 0 | Revolving loans | F | N | Y | 3 | 153000.0 | 135000.0 | 6750.0 | NaN | NaN | Pensioner | Secondary / secondary special | Widow | Municipal apartment | 0.007305 | -13917 | 365243 | -6768.0 | -592 | 1 | 0 | 0 | 1 | 0 | 0 | NaN | 4.0 | 3 | 3 | MONDAY | 7 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.600260 | NaN | 0.9811 | 0.1667 | 0.9811 | 0.1667 | 0.9811 | 0.1667 | 0.0463 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1232.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 102123 | 218557 | 0 | Revolving loans | F | N | Y | 0 | 54000.0 | 135000.0 | 6750.0 | NaN | NaN | Working | Secondary / secondary special | Civil marriage | House / apartment | 0.020713 | -8659 | -451 | -7991.0 | -1327 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 2.0 | 3 | 3 | SUNDAY | 6 | 0 | 0 | 0 | 1 | 1 | 0 | Restaurant | 0.479387 | 0.251239 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1047.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 102245 | 218700 | 0 | Revolving loans | M | N | Y | 1 | 180000.0 | 405000.0 | 20250.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.028663 | -18571 | -5015 | -5083.0 | -1788 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 2.0 | 2 | 2 | THURSDAY | 16 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.536173 | 0.771362 | 0.9712 | 0.1250 | 0.9712 | 0.1250 | 0.9712 | 0.1250 | 0.0514 | No | 0.0 | 0.0 | 0.0 | 0.0 | -394.0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 |
| 106630 | 223714 | 0 | Revolving loans | F | N | Y | 0 | 157500.0 | 180000.0 | 9000.0 | NaN | NaN | Pensioner | Secondary / secondary special | Widow | House / apartment | 0.028663 | -22105 | -2341 | -6447.0 | -4837 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 1.0 | 2 | 2 | THURSDAY | 8 | 0 | 0 | 0 | 0 | 1 | 1 | Business Entity Type 3 | 0.460624 | 0.584990 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 7.0 | 0.0 | 7.0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 107559 | 224784 | 0 | Revolving loans | M | N | N | 2 | 180000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | Municipal apartment | 0.007114 | -13652 | -328 | -2067.0 | -4310 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 3.0 | 2 | 2 | MONDAY | 13 | 0 | 0 | 0 | 0 | 1 | 1 | Business Entity Type 3 | 0.271067 | 0.643026 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -217.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 107715 | 224966 | 0 | Revolving loans | F | N | Y | 2 | 67500.0 | 135000.0 | 6750.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Married | Municipal apartment | 0.009175 | -10644 | -1165 | -3398.0 | -2392 | 1 | 1 | 1 | 1 | 0 | 0 | Medicine staff | 4.0 | 2 | 2 | THURSDAY | 15 | 0 | 0 | 0 | 0 | 0 | 0 | Other | 0.671807 | 0.418854 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1752.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 110972 | 228738 | 0 | Revolving loans | F | N | N | 0 | 135000.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.035792 | -14735 | -1433 | -4146.0 | -4202 | 1 | 1 | 1 | 1 | 0 | 0 | Laborers | 1.0 | 2 | 2 | MONDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Transport: type 4 | 0.665971 | 0.661024 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -2553.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 112566 | 230576 | 0 | Revolving loans | F | N | Y | 1 | 54000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Higher education | Married | House / apartment | 0.009175 | -12222 | -2090 | -1789.0 | -3835 | 1 | 1 | 1 | 1 | 0 | 0 | NaN | 3.0 | 2 | 2 | MONDAY | 17 | 0 | 0 | 0 | 0 | 0 | 0 | Industry: type 11 | 0.771678 | 0.389339 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1889.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 113036 | 231102 | 0 | Revolving loans | F | N | N | 0 | 126000.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.019101 | -12646 | -4335 | -741.0 | -4894 | 1 | 1 | 0 | 1 | 1 | 0 | High skill tech staff | 2.0 | 2 | 2 | THURSDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Transport: type 3 | 0.532212 | 0.540654 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.0 | 2.0 | 0.0 | -1797.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 113251 | 231347 | 0 | Revolving loans | F | N | Y | 0 | 90000.0 | 270000.0 | 13500.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Civil marriage | House / apartment | 0.024610 | -15661 | -377 | -4683.0 | -4677 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 2.0 | 2 | 2 | SATURDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 0.575070 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1281.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 1.0 |
| 113449 | 231576 | 0 | Revolving loans | F | N | Y | 1 | 135000.0 | 405000.0 | 20250.0 | NaN | NaN | Working | Higher education | Married | House / apartment | 0.030755 | -10363 | -1861 | -4926.0 | -2964 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 3.0 | 2 | 2 | MONDAY | 16 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.695052 | 0.272134 | 0.9906 | 0.3750 | 0.9906 | 0.3750 | 0.9906 | 0.3750 | 0.1695 | No | 0.0 | 0.0 | 0.0 | 0.0 | -740.0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 114314 | 232572 | 0 | Revolving loans | M | N | N | 0 | 783000.0 | 1237500.0 | 61875.0 | NaN | NaN | Working | Higher education | Married | House / apartment | 0.046220 | -11633 | -91 | -467.0 | -4303 | 1 | 1 | 1 | 1 | 1 | 0 | IT staff | 2.0 | 1 | 1 | TUESDAY | 18 | 0 | 1 | 1 | 0 | 1 | 1 | Other | 0.732190 | 0.504681 | 0.9965 | 0.5417 | 0.9965 | 0.5417 | 0.9965 | 0.5417 | 0.0917 | No | 0.0 | 0.0 | 0.0 | 0.0 | -179.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.0 |
| 114788 | 233103 | 0 | Revolving loans | F | N | Y | 0 | 112500.0 | 225000.0 | 11250.0 | NaN | NaN | Commercial associate | Higher education | Separated | House / apartment | 0.014520 | -9556 | -2103 | -4404.0 | -2192 | 1 | 1 | 0 | 1 | 1 | 0 | Core staff | 1.0 | 2 | 2 | THURSDAY | 8 | 0 | 0 | 0 | 0 | 0 | 0 | Trade: type 2 | 0.622302 | 0.248536 | 0.9851 | 0.1667 | 0.9851 | 0.1667 | 0.9851 | 0.1667 | 0.0737 | No | 0.0 | 0.0 | 0.0 | 0.0 | -492.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 |
| 116575 | 235188 | 0 | Revolving loans | F | N | Y | 0 | 112500.0 | 180000.0 | 9000.0 | NaN | NaN | Pensioner | Secondary / secondary special | Widow | House / apartment | 0.025164 | -23089 | 365243 | -4693.0 | -4692 | 1 | 0 | 0 | 1 | 0 | 0 | NaN | 1.0 | 2 | 2 | SATURDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.439298 | 0.683269 | 0.9707 | 0.0000 | 0.9707 | 0.0000 | 0.9707 | 0.0000 | 0.0076 | No | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 117302 | 236029 | 0 | Revolving loans | M | N | N | 0 | 166500.0 | 270000.0 | 13500.0 | NaN | NaN | State servant | Higher education | Married | House / apartment | 0.004960 | -12264 | -3386 | -1732.0 | -4522 | 1 | 1 | 0 | 1 | 1 | 1 | Drivers | 2.0 | 2 | 2 | FRIDAY | 12 | 0 | 0 | 0 | 0 | 1 | 1 | Police | 0.621624 | 0.729567 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -363.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 117471 | 236212 | 0 | Revolving loans | F | N | Y | 0 | 135000.0 | 405000.0 | 20250.0 | NaN | NaN | Pensioner | Secondary / secondary special | Married | House / apartment | 0.030755 | -22304 | 365243 | -7563.0 | -4441 | 1 | 0 | 0 | 1 | 1 | 0 | NaN | 2.0 | 2 | 2 | FRIDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.695854 | 0.780144 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1835.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.0 |
| 117483 | 236227 | 0 | Revolving loans | F | N | Y | 1 | 157500.0 | 405000.0 | 20250.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.016612 | -10576 | -1902 | -1086.0 | -18 | 1 | 1 | 1 | 1 | 1 | 0 | Drivers | 3.0 | 2 | 2 | SUNDAY | 15 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.592797 | 0.406617 | 0.9727 | 0.0417 | 0.9727 | 0.0417 | 0.9727 | 0.0417 | 0.0042 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1783.0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 123354 | 243050 | 0 | Revolving loans | F | N | N | 0 | 45000.0 | 135000.0 | 6750.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | With parents | 0.009657 | -7898 | -1198 | -4493.0 | -583 | 1 | 1 | 1 | 1 | 0 | 0 | NaN | 1.0 | 2 | 2 | THURSDAY | 15 | 0 | 0 | 0 | 0 | 0 | 0 | Other | 0.242065 | 0.425893 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -223.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 124123 | 243942 | 0 | Revolving loans | M | N | Y | 0 | 157500.0 | 202500.0 | 10125.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Married | House / apartment | 0.005313 | -15911 | -6468 | -621.0 | -369 | 1 | 1 | 1 | 1 | 1 | 0 | Managers | 2.0 | 2 | 2 | SATURDAY | 20 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.639995 | NaN | 0.9990 | 0.3333 | 0.9990 | 0.3333 | 0.9990 | 0.3333 | 0.2356 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1736.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 124436 | 244308 | 0 | Revolving loans | F | N | Y | 0 | 90000.0 | 135000.0 | 6750.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Single / not married | House / apartment | 0.009334 | -9556 | -308 | -9525.0 | -2215 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 1.0 | 2 | 2 | SATURDAY | 17 | 0 | 0 | 0 | 1 | 1 | 0 | Self-employed | 0.644622 | NaN | 0.9806 | 0.2083 | 0.9806 | 0.2083 | 0.9806 | 0.2083 | 0.1444 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1198.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 124770 | 244697 | 1 | Revolving loans | F | N | Y | 1 | 103500.0 | 135000.0 | 6750.0 | NaN | NaN | State servant | Secondary / secondary special | Married | House / apartment | 0.005313 | -16083 | -531 | -1156.0 | -4571 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 3.0 | 2 | 2 | FRIDAY | 16 | 0 | 0 | 0 | 0 | 1 | 1 | Agriculture | 0.629083 | 0.583238 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1957.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 128354 | 248885 | 0 | Revolving loans | F | N | Y | 0 | 112500.0 | 225000.0 | 11250.0 | NaN | NaN | Working | Higher education | Married | House / apartment | 0.018634 | -12516 | -491 | -319.0 | -2716 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 2.0 | 2 | 2 | TUESDAY | 17 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.665713 | NaN | 0.9786 | 0.2500 | 0.9767 | 0.1667 | 0.9786 | 0.2500 | 0.0719 | No | 0.0 | 0.0 | 0.0 | 0.0 | -702.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 129705 | 250439 | 0 | Revolving loans | F | N | N | 1 | 54000.0 | 180000.0 | 9000.0 | NaN | NaN | Commercial associate | Higher education | Married | House / apartment | 0.015221 | -10011 | -978 | -4345.0 | -727 | 1 | 1 | 1 | 1 | 0 | 0 | Sales staff | 3.0 | 2 | 2 | TUESDAY | 11 | 0 | 0 | 0 | 0 | 1 | 1 | Trade: type 2 | 0.370048 | 0.535276 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.0 | 1.0 | 0.0 | -900.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 130737 | 251638 | 0 | Revolving loans | F | N | N | 0 | 553500.0 | 675000.0 | 33750.0 | NaN | NaN | Commercial associate | Higher education | Married | House / apartment | 0.006629 | -14345 | -3560 | -6525.0 | -4622 | 1 | 1 | 0 | 1 | 1 | 0 | Accountants | 2.0 | 2 | 2 | FRIDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | Security | 0.658630 | 0.513694 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -2342.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 |
| 130800 | 251708 | 0 | Revolving loans | F | N | Y | 0 | 135000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Lower secondary | Single / not married | With parents | 0.019689 | -7904 | -1002 | -7074.0 | -562 | 1 | 1 | 0 | 1 | 1 | 0 | Sales staff | 1.0 | 2 | 2 | FRIDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.216059 | 0.263647 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 7.0 | 0.0 | 7.0 | 0.0 | -542.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 131669 | 252707 | 0 | Revolving loans | F | N | Y | 1 | 90000.0 | 157500.0 | 7875.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.030755 | -9018 | -361 | -9003.0 | -1215 | 1 | 1 | 0 | 1 | 0 | 0 | NaN | 3.0 | 2 | 2 | SATURDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Kindergarten | 0.238266 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -252.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 132208 | 253336 | 0 | Revolving loans | M | N | Y | 1 | 247500.0 | 585000.0 | 29250.0 | NaN | NaN | Working | Higher education | Civil marriage | House / apartment | 0.014464 | -17845 | -873 | -5488.0 | -1353 | 1 | 1 | 1 | 1 | 1 | 0 | Managers | 3.0 | 2 | 2 | FRIDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.707407 | 0.551381 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -2314.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 133526 | 254865 | 0 | Revolving loans | M | N | Y | 1 | 180000.0 | 382500.0 | 19125.0 | NaN | NaN | Working | Higher education | Married | House / apartment | 0.031329 | -14756 | -2327 | -8861.0 | -4352 | 1 | 1 | 0 | 1 | 1 | 0 | Managers | 3.0 | 2 | 2 | SATURDAY | 13 | 0 | 0 | 0 | 0 | 1 | 1 | Business Entity Type 3 | 0.754487 | 0.256706 | 0.9717 | 0.0833 | 0.9717 | 0.0833 | 0.9717 | 0.0833 | 0.0323 | No | 3.0 | 0.0 | 3.0 | 0.0 | -1832.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 133548 | 254889 | 0 | Revolving loans | F | N | Y | 0 | 157500.0 | 450000.0 | 22500.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Married | House / apartment | 0.072508 | -11097 | -1453 | -2996.0 | -2915 | 1 | 1 | 0 | 1 | 1 | 0 | NaN | 2.0 | 1 | 1 | FRIDAY | 19 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.672240 | NaN | 0.9955 | 0.8125 | 0.9955 | 0.6667 | 0.9955 | 0.8125 | 0.2890 | No | 0.0 | 0.0 | 0.0 | 0.0 | -350.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 6.0 | 0.0 | 1.0 |
| 134298 | 255767 | 0 | Revolving loans | F | N | Y | 0 | 202500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.010006 | -10382 | -671 | -3609.0 | -3045 | 1 | 1 | 1 | 1 | 0 | 0 | Core staff | 1.0 | 2 | 2 | TUESDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | Trade: type 3 | 0.454783 | 0.397946 | 0.9871 | 0.3333 | 0.9871 | 0.3333 | 0.9871 | 0.3333 | 0.1058 | No | 0.0 | 0.0 | 0.0 | 0.0 | -910.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 134327 | 255803 | 0 | Revolving loans | F | N | Y | 0 | 135000.0 | 337500.0 | 16875.0 | NaN | NaN | Pensioner | Secondary / secondary special | Married | House / apartment | 0.006671 | -21224 | 365243 | -6961.0 | -4399 | 1 | 0 | 0 | 1 | 1 | 0 | Cleaning staff | 2.0 | 2 | 2 | FRIDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.723151 | NaN | 0.9891 | 0.1667 | 0.9891 | 0.1667 | 0.9891 | 0.1667 | 0.0505 | No | 2.0 | 0.0 | 2.0 | 0.0 | -1289.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 |
| 136330 | 258130 | 0 | Revolving loans | M | N | Y | 0 | 112500.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Civil marriage | House / apartment | 0.018029 | -14673 | -1446 | -3024.0 | -231 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 2.0 | 3 | 3 | TUESDAY | 8 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.384364 | 0.519097 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -1418.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 |
| 136424 | 258241 | 0 | Revolving loans | F | N | Y | 0 | 90000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.030755 | -19428 | -238 | -5212.0 | -1521 | 1 | 1 | 1 | 1 | 0 | 0 | Laborers | 2.0 | 2 | 2 | SATURDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 2 | 0.670407 | 0.362277 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 | 0.0 | 3.0 | 0.0 | -2199.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 1.0 |
| 137091 | 259002 | 0 | Revolving loans | M | N | Y | 0 | 67500.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Higher education | Separated | House / apartment | 0.009175 | -18673 | -955 | -6006.0 | -2218 | 1 | 1 | 1 | 1 | 0 | 0 | Managers | 1.0 | 2 | 2 | WEDNESDAY | 16 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.738843 | 0.511892 | 0.9916 | 0.3750 | 0.9916 | 0.3750 | 0.9916 | 0.3750 | 0.1148 | No | 1.0 | 1.0 | 1.0 | 1.0 | -862.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 137354 | 259301 | 0 | Revolving loans | M | N | Y | 1 | 99000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Higher education | Civil marriage | House / apartment | 0.009549 | -11687 | -155 | -2972.0 | -4353 | 1 | 1 | 1 | 1 | 1 | 1 | NaN | 3.0 | 2 | 2 | TUESDAY | 11 | 0 | 1 | 1 | 0 | 1 | 1 | Trade: type 7 | 0.490010 | 0.270707 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -1106.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 137374 | 259325 | 0 | Revolving loans | F | N | Y | 0 | 225000.0 | 585000.0 | 29250.0 | NaN | NaN | State servant | Higher education | Single / not married | House / apartment | 0.005084 | -16419 | -6879 | -10178.0 | -4243 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 1.0 | 2 | 2 | FRIDAY | 17 | 0 | 0 | 0 | 0 | 0 | 0 | Police | 0.736295 | 0.148254 | 0.9786 | 0.1667 | 0.9786 | 0.1667 | 0.9786 | 0.1667 | 0.1162 | No | 0.0 | 0.0 | 0.0 | 0.0 | -757.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 139558 | 261811 | 0 | Revolving loans | M | N | N | 0 | 67500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.031329 | -10296 | -90 | -2165.0 | -2970 | 1 | 1 | 1 | 1 | 1 | 0 | Managers | 1.0 | 2 | 2 | THURSDAY | 8 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.446766 | 0.604113 | 0.9876 | 0.0833 | 0.9876 | 0.0000 | 0.9876 | 0.0833 | 0.1157 | No | 1.0 | 0.0 | 1.0 | 0.0 | -1790.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 142605 | 265357 | 0 | Revolving loans | F | N | Y | 2 | 72000.0 | 135000.0 | 6750.0 | NaN | NaN | Working | Higher education | Married | House / apartment | 0.007120 | -11819 | -4369 | -3239.0 | -4030 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 4.0 | 2 | 2 | MONDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | School | 0.663505 | 0.418854 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1281.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 143606 | 266519 | 0 | Revolving loans | F | N | Y | 0 | 153000.0 | 270000.0 | 13500.0 | NaN | NaN | State servant | Secondary / secondary special | Married | House / apartment | 0.014464 | -15165 | -3268 | -9292.0 | -4914 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 2.0 | 2 | 2 | FRIDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Police | 0.658831 | 0.577969 | 0.9806 | 0.1667 | 0.9806 | 0.1667 | 0.9806 | 0.1667 | 0.0560 | No | 0.0 | 0.0 | 0.0 | 0.0 | -191.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 145137 | 268295 | 0 | Revolving loans | M | N | N | 0 | 90000.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.009175 | -11203 | -1943 | -4902.0 | -3396 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 2.0 | 2 | 2 | SUNDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Trade: type 2 | 0.385027 | 0.488455 | 0.9861 | 0.1667 | 0.9861 | 0.1667 | 0.9861 | 0.1667 | 0.0535 | No | 5.0 | 0.0 | 5.0 | 0.0 | -1421.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 |
| 145278 | 268463 | 0 | Revolving loans | F | N | N | 0 | 225000.0 | 202500.0 | 10125.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Married | House / apartment | 0.030755 | -16400 | -6763 | -10411.0 | -4799 | 1 | 1 | 0 | 1 | 1 | 0 | Core staff | 2.0 | 2 | 2 | FRIDAY | 16 | 0 | 0 | 0 | 0 | 1 | 1 | Self-employed | 0.157674 | 0.715103 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.0 | 2.0 | 0.0 | -2289.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 145594 | 268818 | 0 | Revolving loans | F | N | Y | 0 | 112500.0 | 337500.0 | 16875.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.010147 | -16928 | -2780 | -3003.0 | -473 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 2.0 | 2 | 2 | MONDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.246148 | 0.720944 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -1857.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 |
| 147294 | 270776 | 0 | Revolving loans | F | N | N | 2 | 202500.0 | 135000.0 | 6750.0 | NaN | NaN | Working | Secondary / secondary special | Separated | With parents | 0.035792 | -10376 | -466 | -2266.0 | -2197 | 1 | 1 | 1 | 1 | 0 | 0 | Sales staff | 3.0 | 2 | 2 | SUNDAY | 13 | 0 | 1 | 1 | 0 | 1 | 1 | Business Entity Type 3 | 0.256754 | 0.656158 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 0.0 | 4.0 | 0.0 | -1225.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 149045 | 272806 | 0 | Revolving loans | F | N | Y | 2 | 45000.0 | 135000.0 | 6750.0 | NaN | NaN | State servant | Secondary / secondary special | Married | House / apartment | 0.008575 | -8657 | -1751 | -1035.0 | -1337 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 4.0 | 2 | 2 | TUESDAY | 12 | 0 | 0 | 0 | 0 | 1 | 1 | Government | 0.568683 | 0.553165 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -647.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 149111 | 272877 | 0 | Revolving loans | F | N | Y | 2 | 81000.0 | 247500.0 | 12375.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.018634 | -13277 | -1589 | -2847.0 | -4253 | 1 | 1 | 1 | 1 | 0 | 0 | Sales staff | 4.0 | 2 | 2 | THURSDAY | 16 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.066912 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -653.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 3.0 |
| 149562 | 273397 | 0 | Revolving loans | F | N | N | 0 | 157500.0 | 270000.0 | 13500.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Married | House / apartment | 0.025164 | -10173 | -932 | -10152.0 | -2393 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 2.0 | 2 | 2 | THURSDAY | 7 | 0 | 0 | 0 | 1 | 1 | 0 | Self-employed | 0.718044 | 0.318596 | 0.9727 | 0.1250 | 0.9727 | 0.1250 | 0.9727 | 0.1250 | 0.0421 | No | 4.0 | 0.0 | 4.0 | 0.0 | -481.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 150425 | 274382 | 0 | Revolving loans | M | N | N | 0 | 180000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | With parents | 0.035792 | -10275 | -1937 | -4103.0 | -2946 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 1.0 | 2 | 2 | THURSDAY | 17 | 0 | 0 | 0 | 1 | 1 | 0 | Other | 0.653978 | 0.263647 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -174.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 4.0 |
| 152608 | 276875 | 0 | Revolving loans | F | N | Y | 0 | 112500.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.046220 | -17936 | -10109 | -11682.0 | -1127 | 1 | 1 | 1 | 1 | 1 | 0 | Medicine staff | 2.0 | 1 | 1 | MONDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | Medicine | 0.714221 | NaN | 0.9747 | 0.1250 | 0.9747 | 0.1250 | 0.9747 | 0.1250 | 0.0314 | No | 0.0 | 0.0 | 0.0 | 0.0 | -993.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 152898 | 277210 | 1 | Revolving loans | F | N | Y | 0 | 225000.0 | 315000.0 | 15750.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | Office apartment | 0.019101 | -21416 | -4621 | -8460.0 | -4085 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 1.0 | 2 | 2 | SATURDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.722301 | 0.431192 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -816.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 4.0 |
| 153801 | 278254 | 1 | Revolving loans | F | N | Y | 0 | 90000.0 | 135000.0 | 6750.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Single / not married | House / apartment | 0.006008 | -10223 | -1037 | -4074.0 | -1717 | 1 | 1 | 1 | 1 | 0 | 0 | Sales staff | 1.0 | 2 | 2 | THURSDAY | 16 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.522128 | NaN | 0.9737 | 0.0417 | 0.9737 | 0.0417 | 0.9737 | 0.0417 | 0.0064 | No | 5.0 | 3.0 | 5.0 | 3.0 | -793.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 155673 | 280456 | 0 | Revolving loans | F | N | Y | 1 | 58500.0 | 157500.0 | 7875.0 | NaN | NaN | Working | Higher education | Single / not married | House / apartment | 0.006207 | -9636 | -1803 | -3788.0 | -806 | 1 | 1 | 1 | 1 | 1 | 0 | Accountants | 2.0 | 2 | 2 | SATURDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.538261 | 0.450747 | 0.9841 | 0.1667 | 0.9841 | 0.1667 | 0.9841 | 0.1667 | 0.0491 | No | 2.0 | 0.0 | 2.0 | 0.0 | -626.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 155716 | 280504 | 0 | Revolving loans | F | N | Y | 0 | 96750.0 | 157500.0 | 7875.0 | NaN | NaN | Pensioner | Secondary / secondary special | Separated | House / apartment | 0.019689 | -23710 | 365243 | -14806.0 | -4297 | 1 | 0 | 0 | 1 | 1 | 0 | NaN | 1.0 | 2 | 2 | WEDNESDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.283742 | 0.537070 | 0.9702 | 0.0417 | 0.9702 | 0.0417 | 0.9702 | 0.0417 | 0.0239 | No | 0.0 | 0.0 | 0.0 | 0.0 | -2183.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 156456 | 281353 | 0 | Revolving loans | M | N | Y | 4 | 180000.0 | 900000.0 | 45000.0 | NaN | NaN | Working | Higher education | Married | House / apartment | 0.010006 | -15496 | -2804 | -836.0 | -2344 | 1 | 1 | 1 | 1 | 1 | 0 | Managers | 6.0 | 2 | 2 | FRIDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Transport: type 4 | 0.788781 | 0.158555 | 0.9896 | 0.3333 | 0.9896 | 0.3333 | 0.9896 | 0.3333 | 0.1503 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1904.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 158563 | 283824 | 0 | Revolving loans | M | N | Y | 0 | 292500.0 | 675000.0 | 33750.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Single / not married | House / apartment | 0.019689 | -15003 | -1753 | -6498.0 | -2216 | 1 | 1 | 1 | 1 | 0 | 0 | Laborers | 1.0 | 2 | 2 | THURSDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.378930 | 0.265049 | 0.9871 | 0.3750 | 0.9871 | 0.3750 | 0.9871 | 0.3750 | 0.3812 | No | 3.0 | 0.0 | 3.0 | 0.0 | -233.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 2.0 |
| 158785 | 284082 | 0 | Revolving loans | F | N | Y | 0 | 247500.0 | 405000.0 | 20250.0 | NaN | NaN | Commercial associate | Higher education | Married | House / apartment | 0.018850 | -10867 | -286 | -5510.0 | -3475 | 1 | 1 | 1 | 1 | 1 | 0 | High skill tech staff | 2.0 | 2 | 2 | THURSDAY | 12 | 0 | 0 | 0 | 0 | 1 | 1 | Business Entity Type 2 | 0.324049 | 0.399676 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -477.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 161730 | 287492 | 0 | Revolving loans | F | N | Y | 0 | 67500.0 | 135000.0 | 6750.0 | NaN | NaN | Pensioner | Secondary / secondary special | Married | House / apartment | 0.031329 | -22374 | 365243 | -4751.0 | -4816 | 1 | 0 | 0 | 1 | 0 | 0 | NaN | 2.0 | 2 | 2 | WEDNESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.545123 | 0.852828 | 0.9881 | 0.1667 | 0.9881 | 0.1667 | 0.9881 | 0.1667 | 0.0744 | No | 0.0 | 0.0 | 0.0 | 0.0 | -2609.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 163709 | 289773 | 0 | Revolving loans | F | N | Y | 0 | 90000.0 | 337500.0 | 16875.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Married | House / apartment | 0.007020 | -16454 | -1977 | -5819.0 | -11 | 1 | 1 | 1 | 1 | 1 | 0 | Cooking staff | 2.0 | 2 | 2 | SATURDAY | 11 | 0 | 0 | 0 | 0 | 1 | 1 | Other | 0.252190 | 0.626304 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -38.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 163889 | 289983 | 0 | Revolving loans | F | N | Y | 0 | 90000.0 | 225000.0 | 11250.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.005084 | -9846 | -2612 | -9715.0 | -2520 | 1 | 1 | 1 | 1 | 1 | 0 | Medicine staff | 1.0 | 2 | 2 | MONDAY | 16 | 0 | 0 | 0 | 0 | 0 | 0 | Medicine | 0.545936 | NaN | 0.9712 | 0.0000 | 0.9712 | 0.0000 | 0.9712 | 0.0000 | 0.0029 | Yes | 3.0 | 2.0 | 3.0 | 2.0 | -1483.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 167777 | 294482 | 0 | Revolving loans | F | N | Y | 1 | 99000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Civil marriage | House / apartment | 0.009334 | -7994 | -1284 | -1657.0 | -426 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 3.0 | 2 | 2 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Transport: type 2 | 0.506059 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 | 1.0 | 3.0 | 0.0 | -245.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 168169 | 294934 | 0 | Revolving loans | M | N | Y | 1 | 202500.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Married | Municipal apartment | 0.015221 | -10212 | -2294 | -67.0 | -2868 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 3.0 | 2 | 2 | SATURDAY | 12 | 0 | 0 | 0 | 0 | 1 | 1 | Industry: type 9 | 0.449199 | 0.546023 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 |
| 168588 | 295417 | 0 | Revolving loans | F | N | Y | 1 | 67500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.018634 | -8141 | -592 | -6985.0 | -812 | 1 | 1 | 1 | 1 | 0 | 0 | Cooking staff | 2.0 | 2 | 2 | TUESDAY | 9 | 0 | 1 | 1 | 0 | 1 | 1 | Restaurant | 0.161679 | 0.602386 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -361.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 169101 | 296002 | 0 | Revolving loans | F | N | N | 0 | 112500.0 | 337500.0 | 16875.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.022625 | -18986 | -1039 | -8775.0 | -2521 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 2.0 | 2 | 2 | TUESDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.462634 | 0.751724 | 0.9786 | 0.1667 | 0.9786 | 0.1667 | 0.9786 | 0.1667 | 0.0782 | No | 4.0 | 0.0 | 4.0 | 0.0 | -1089.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 176663 | 304718 | 0 | Revolving loans | F | N | Y | 0 | 180000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.010500 | -14207 | -2178 | -8014.0 | -788 | 1 | 1 | 1 | 1 | 0 | 0 | Managers | 2.0 | 3 | 3 | MONDAY | 12 | 0 | 0 | 0 | 1 | 1 | 0 | Self-employed | 0.515659 | 0.495666 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | -508.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 179626 | 308153 | 0 | Revolving loans | M | N | Y | 0 | 90000.0 | 270000.0 | 13500.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Married | House / apartment | 0.019689 | -20105 | -903 | -8938.0 | -3544 | 1 | 1 | 1 | 1 | 1 | 0 | Drivers | 2.0 | 2 | 2 | THURSDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.454013 | 0.627991 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -697.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 180925 | 309672 | 0 | Revolving loans | F | N | Y | 2 | 90000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.019101 | -12760 | -4047 | -5247.0 | -3965 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 4.0 | 2 | 2 | TUESDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Construction | 0.256341 | 0.410103 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -926.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 183381 | 312551 | 0 | Revolving loans | F | N | Y | 1 | 135000.0 | 180000.0 | 9000.0 | NaN | NaN | Commercial associate | Higher education | Separated | House / apartment | 0.008575 | -10809 | -999 | -841.0 | -789 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 2.0 | 2 | 2 | FRIDAY | 10 | 0 | 0 | 0 | 1 | 1 | 1 | Trade: type 2 | 0.583894 | 0.192942 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -149.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 184811 | 314222 | 0 | Revolving loans | M | N | Y | 0 | 112500.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Incomplete higher | Married | House / apartment | 0.015221 | -12622 | -1567 | -4583.0 | -4590 | 1 | 1 | 0 | 1 | 1 | 0 | Laborers | 2.0 | 2 | 2 | TUESDAY | 15 | 0 | 0 | 0 | 0 | 1 | 1 | Self-employed | 0.523923 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -907.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 186634 | 316367 | 1 | Revolving loans | M | N | Y | 1 | 67500.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.031329 | -14709 | -6608 | -5394.0 | -3469 | 1 | 1 | 1 | 1 | 1 | 0 | Security staff | 3.0 | 2 | 2 | MONDAY | 12 | 0 | 0 | 0 | 0 | 1 | 1 | Business Entity Type 2 | 0.401519 | 0.591977 | 0.9925 | 0.0417 | 0.9926 | 0.0417 | 0.9925 | 0.0417 | 0.0141 | No | 0.0 | 0.0 | 0.0 | 0.0 | -172.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 187032 | 316821 | 0 | Revolving loans | F | N | Y | 0 | 90000.0 | 337500.0 | 16875.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Widow | House / apartment | 0.009630 | -14401 | -4982 | -5055.0 | -5163 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 1.0 | 2 | 2 | FRIDAY | 19 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.620826 | NaN | 0.9717 | 0.0833 | 0.9717 | 0.0833 | 0.9717 | 0.0833 | 0.0241 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1910.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 187120 | 316922 | 0 | Revolving loans | M | N | Y | 0 | 157500.0 | 450000.0 | 22500.0 | NaN | NaN | Pensioner | Secondary / secondary special | Married | Office apartment | 0.018029 | -23036 | 365243 | -1956.0 | -5371 | 1 | 0 | 0 | 1 | 1 | 0 | NaN | 2.0 | 3 | 3 | FRIDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.248121 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6.0 | 0.0 | 6.0 | 0.0 | -1231.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 187145 | 316954 | 0 | Revolving loans | M | N | Y | 0 | 109467.0 | 135000.0 | 6750.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Married | House / apartment | 0.018801 | -9780 | -1310 | -3641.0 | -1600 | 1 | 1 | 1 | 1 | 0 | 0 | Laborers | 2.0 | 2 | 2 | FRIDAY | 8 | 0 | 0 | 0 | 1 | 1 | 0 | Trade: type 7 | 0.242764 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 7.0 | 2.0 | 7.0 | 2.0 | -1010.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 187167 | 316976 | 0 | Revolving loans | F | N | Y | 0 | 81000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Widow | House / apartment | 0.008575 | -14295 | -1129 | -8335.0 | -5058 | 1 | 1 | 1 | 1 | 0 | 0 | Sales staff | 1.0 | 2 | 2 | SUNDAY | 13 | 0 | 0 | 0 | 1 | 1 | 0 | Trade: type 3 | 0.585710 | 0.223831 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -972.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 |
| 187348 | 317181 | 0 | Revolving loans | F | N | Y | 0 | 202500.0 | 585000.0 | 29250.0 | NaN | NaN | Commercial associate | Higher education | Unknown | House / apartment | 0.031329 | -12844 | -232 | -1597.0 | -1571 | 1 | 1 | 0 | 1 | 0 | 0 | Accountants | NaN | 2 | 2 | FRIDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 2 | 0.645168 | 0.670652 | 0.9970 | 0.3750 | 0.9970 | 0.3750 | 0.9970 | 0.3750 | 0.0791 | No | 1.0 | 0.0 | 1.0 | 0.0 | -654.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 188475 | 318512 | 0 | Revolving loans | F | N | Y | 0 | 90000.0 | 180000.0 | 9000.0 | NaN | NaN | Pensioner | Secondary / secondary special | Single / not married | House / apartment | 0.018209 | -22925 | 365243 | -12192.0 | -4867 | 1 | 0 | 0 | 1 | 1 | 0 | NaN | 1.0 | 3 | 3 | SATURDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.738379 | 0.762336 | 0.9846 | 0.3333 | 0.9846 | 0.3333 | 0.9846 | 0.3333 | 0.1030 | No | 2.0 | 1.0 | 2.0 | 0.0 | -1344.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 190113 | 320433 | 1 | Revolving loans | F | N | Y | 1 | 112500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.026392 | -8329 | -1287 | -608.0 | -20 | 1 | 1 | 1 | 1 | 0 | 0 | Private service staff | 2.0 | 2 | 2 | THURSDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | Services | 0.531335 | NaN | 0.9771 | 0.1667 | 0.9772 | 0.1667 | 0.9771 | 0.1667 | 0.0384 | No | 1.0 | 1.0 | 1.0 | 1.0 | -524.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 191858 | 322471 | 0 | Revolving loans | F | N | N | 0 | 63000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | Rented apartment | 0.007305 | -7712 | -458 | -6432.0 | -387 | 1 | 1 | 1 | 1 | 1 | 0 | Cleaning staff | 1.0 | 3 | 3 | THURSDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Other | 0.145654 | 0.134265 | 0.9717 | 0.0000 | 0.9717 | 0.0000 | 0.9717 | 0.0000 | 0.0017 | No | 1.0 | 1.0 | 1.0 | 1.0 | -373.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 193826 | 324762 | 0 | Revolving loans | F | N | Y | 0 | 112500.0 | 180000.0 | 9000.0 | NaN | NaN | State servant | Secondary / secondary special | Widow | House / apartment | 0.006852 | -16145 | -1372 | -4490.0 | -4504 | 1 | 1 | 0 | 1 | 1 | 0 | Security staff | 1.0 | 3 | 3 | SATURDAY | 13 | 0 | 0 | 0 | 0 | 1 | 1 | School | 0.145703 | 0.328063 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -990.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 7.0 |
| 195008 | 326127 | 0 | Revolving loans | F | N | Y | 1 | 67500.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.010500 | -13341 | -6352 | -2567.0 | -4778 | 1 | 1 | 1 | 1 | 1 | 0 | Accountants | 3.0 | 3 | 3 | SUNDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Other | 0.552557 | 0.694093 | 0.9896 | 0.1667 | 0.9881 | 0.1667 | 0.9896 | 0.1667 | 0.0224 | No | 0.0 | 0.0 | 0.0 | 0.0 | -305.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 195851 | 327096 | 0 | Revolving loans | F | N | Y | 0 | 125550.0 | 225000.0 | 11250.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.016612 | -18481 | -3008 | -5450.0 | -2026 | 1 | 1 | 1 | 1 | 1 | 0 | High skill tech staff | 2.0 | 2 | 2 | SATURDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Electricity | 0.464407 | 0.700184 | 0.9856 | 0.1667 | 0.9856 | 0.1667 | 0.9856 | 0.1667 | 0.0895 | No | 3.0 | 0.0 | 3.0 | 0.0 | -1279.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 195936 | 327191 | 0 | Revolving loans | M | N | N | 0 | 112500.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.025164 | -13651 | -5934 | -1443.0 | -4435 | 1 | 1 | 1 | 1 | 1 | 0 | Drivers | 2.0 | 2 | 2 | THURSDAY | 15 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.488731 | 0.506484 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -633.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 5.0 |
| 197105 | 328529 | 0 | Revolving loans | F | N | Y | 0 | 90000.0 | 337500.0 | 16875.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.030755 | -17190 | -625 | -7346.0 | -739 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 2.0 | 2 | 2 | SUNDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Trade: type 3 | 0.637826 | 0.450747 | 0.9896 | 0.2500 | 0.9950 | 0.1667 | 0.9945 | 0.2500 | 0.0163 | No | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 197647 | 329156 | 0 | Revolving loans | F | N | Y | 1 | 135000.0 | 270000.0 | 13500.0 | NaN | NaN | State servant | Higher education | Married | House / apartment | 0.006629 | -12227 | -5585 | -3163.0 | -4674 | 1 | 1 | 1 | 1 | 1 | 0 | Medicine staff | 3.0 | 2 | 2 | TUESDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Medicine | 0.571754 | 0.614414 | 0.9786 | 0.0417 | 0.9786 | 0.0417 | 0.9786 | 0.0417 | 0.0084 | Yes | 1.0 | 0.0 | 1.0 | 0.0 | -902.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 199643 | 331445 | 0 | Revolving loans | F | N | Y | 1 | 87750.0 | 225000.0 | 11250.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.022800 | -10062 | -165 | -4259.0 | -2706 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 2.0 | 2 | 2 | MONDAY | 16 | 0 | 0 | 0 | 1 | 1 | 0 | Trade: type 2 | 0.265312 | 0.553165 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -2131.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 200312 | 332213 | 0 | Revolving loans | F | N | Y | 0 | 216000.0 | 765000.0 | 38250.0 | NaN | NaN | Working | Incomplete higher | Married | House / apartment | 0.046220 | -17430 | -4791 | -1951.0 | -969 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 2.0 | 1 | 1 | TUESDAY | 12 | 0 | 0 | 0 | 1 | 1 | 0 | Self-employed | 0.363537 | 0.461482 | 0.9950 | 0.5000 | 0.9950 | 0.5000 | 0.9950 | 0.5000 | 0.3154 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1386.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 201823 | 333949 | 0 | Revolving loans | F | N | Y | 1 | 112500.0 | 337500.0 | 16875.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.018634 | -14194 | -369 | -6751.0 | -2124 | 1 | 1 | 1 | 1 | 1 | 0 | Accountants | 3.0 | 2 | 2 | FRIDAY | 15 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.394268 | 0.657784 | 0.9796 | 0.3333 | 0.9796 | 0.3333 | 0.9796 | 0.3333 | 0.0516 | No | 0.0 | 0.0 | 0.0 | 0.0 | -685.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 202896 | 335206 | 0 | Revolving loans | F | N | Y | 0 | 67500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.024610 | -7721 | -616 | -2569.0 | -357 | 1 | 1 | 0 | 1 | 1 | 0 | Sales staff | 1.0 | 2 | 2 | FRIDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.589732 | NaN | 0.9866 | 0.3333 | 0.9866 | 0.3333 | 0.9866 | 0.3333 | 0.0926 | No | 1.0 | 0.0 | 1.0 | 0.0 | -351.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 204519 | 337101 | 0 | Revolving loans | F | N | N | 0 | 202500.0 | 360000.0 | 18000.0 | NaN | NaN | Commercial associate | Higher education | Married | House / apartment | 0.072508 | -8940 | -2248 | -8927.0 | -1443 | 1 | 1 | 0 | 1 | 1 | 0 | Sales staff | 2.0 | 1 | 1 | FRIDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.050720 | 0.401407 | 0.9806 | 0.3333 | 0.9806 | 0.3333 | 0.9806 | 0.3333 | 0.1828 | No | 0.0 | 0.0 | 0.0 | 0.0 | -459.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 206222 | 339033 | 0 | Revolving loans | F | N | Y | 0 | 126000.0 | 337500.0 | 16875.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.026392 | -14229 | -2526 | -3496.0 | -4129 | 1 | 1 | 0 | 1 | 1 | 0 | Core staff | 2.0 | 2 | 2 | SUNDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.388699 | 0.492060 | 0.9881 | 0.0833 | 0.9881 | 0.0833 | 0.9881 | 0.0833 | 0.0201 | No | 0.0 | 0.0 | 0.0 | 0.0 | -339.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 207015 | 339947 | 0 | Revolving loans | F | N | Y | 1 | 283500.0 | 855000.0 | 42750.0 | NaN | NaN | Working | Secondary / secondary special | Widow | House / apartment | 0.030755 | -11284 | -1531 | -2858.0 | -3215 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 2.0 | 2 | 2 | MONDAY | 16 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.660755 | 0.413597 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.0 | 2.0 | 0.0 | -2347.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 |
| 209338 | 342625 | 0 | Revolving loans | F | N | Y | 0 | 112500.0 | 247500.0 | 12375.0 | NaN | NaN | Commercial associate | Higher education | Married | House / apartment | 0.018209 | -15613 | -2564 | -4308.0 | -4631 | 1 | 1 | 1 | 1 | 1 | 0 | Accountants | 2.0 | 3 | 3 | FRIDAY | 8 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.485814 | 0.742182 | 0.9891 | 0.1667 | 0.9891 | 0.1667 | 0.9891 | 0.1667 | 0.0324 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1721.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 5.0 |
| 209657 | 342986 | 0 | Revolving loans | M | N | Y | 0 | 108000.0 | 180000.0 | 9000.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Single / not married | House / apartment | 0.031329 | -8898 | -491 | -3758.0 | -1518 | 1 | 1 | 1 | 1 | 1 | 0 | Low-skill Laborers | 1.0 | 2 | 2 | TUESDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 2 | 0.573300 | 0.448962 | 0.9911 | 0.1667 | 0.9911 | 0.1667 | 0.9911 | 0.1667 | 0.0509 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1007.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 |
| 210718 | 344187 | 1 | Revolving loans | F | N | N | 1 | 108000.0 | 247500.0 | 12375.0 | NaN | NaN | State servant | Secondary / secondary special | Married | Municipal apartment | 0.003818 | -16933 | -1285 | -2617.0 | -223 | 1 | 1 | 1 | 1 | 0 | 0 | Medicine staff | 3.0 | 2 | 2 | TUESDAY | 8 | 0 | 0 | 0 | 0 | 1 | 1 | Medicine | 0.520595 | NaN | 0.9821 | 0.0417 | 0.9821 | 0.0417 | 0.9821 | 0.0417 | 0.0094 | No | 0.0 | 0.0 | 0.0 | 0.0 | -166.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 211090 | 344622 | 0 | Revolving loans | F | N | Y | 0 | 45000.0 | 157500.0 | 7875.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.008625 | -15167 | -4213 | -7864.0 | -5068 | 1 | 1 | 1 | 1 | 0 | 0 | Core staff | 2.0 | 2 | 2 | THURSDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Kindergarten | 0.119960 | 0.357293 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -927.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 214803 | 348904 | 1 | Revolving loans | F | N | Y | 2 | 90000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.008575 | -14016 | -3964 | -1739.0 | -4575 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 4.0 | 2 | 2 | FRIDAY | 11 | 0 | 0 | 0 | 0 | 1 | 1 | Business Entity Type 3 | 0.635945 | 0.355639 | 0.9970 | 0.0833 | 0.9970 | 0.0833 | 0.9970 | 0.0833 | 0.0276 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1063.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 215137 | 349287 | 0 | Revolving loans | M | N | Y | 1 | 135000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.022800 | -16109 | -1401 | -5950.0 | -4218 | 1 | 1 | 1 | 1 | 1 | 1 | Sales staff | 3.0 | 2 | 2 | SATURDAY | 9 | 0 | 0 | 0 | 0 | 1 | 1 | Trade: type 7 | 0.104879 | 0.331251 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 | 0.0 | 3.0 | 0.0 | -812.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 5.0 |
| 215656 | 349877 | 0 | Revolving loans | F | N | N | 0 | 67500.0 | 135000.0 | 6750.0 | NaN | NaN | State servant | Secondary / secondary special | Separated | Co-op apartment | 0.020246 | -15935 | -7018 | -6881.0 | -5099 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 1.0 | 3 | 3 | MONDAY | 8 | 0 | 0 | 0 | 0 | 0 | 0 | Agriculture | 0.310125 | 0.429424 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1332.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 216059 | 350347 | 0 | Revolving loans | M | N | Y | 0 | 112500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.030755 | -13939 | -697 | -3444.0 | -4853 | 1 | 1 | 0 | 1 | 0 | 0 | High skill tech staff | 2.0 | 2 | 2 | FRIDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.575262 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -221.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 216762 | 351158 | 0 | Revolving loans | F | N | N | 0 | 135000.0 | 405000.0 | 20250.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.010006 | -18525 | -2431 | -1805.0 | -2051 | 1 | 1 | 1 | 1 | 1 | 0 | Security staff | 2.0 | 2 | 2 | THURSDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.503430 | 0.581484 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.0 | 2.0 | 0.0 | -425.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 216996 | 351432 | 0 | Revolving loans | M | N | Y | 0 | 67500.0 | 202500.0 | 10125.0 | NaN | NaN | State servant | Secondary / secondary special | Married | House / apartment | 0.018209 | -19084 | -3831 | -9386.0 | -2636 | 1 | 1 | 1 | 1 | 1 | 0 | Security staff | 2.0 | 3 | 3 | WEDNESDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | Security Ministries | 0.409695 | 0.288130 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -748.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 218083 | 352663 | 0 | Revolving loans | M | N | Y | 0 | 180000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.025164 | -7888 | -1065 | -7888.0 | -560 | 1 | 1 | 1 | 1 | 1 | 0 | High skill tech staff | 1.0 | 2 | 2 | WEDNESDAY | 15 | 0 | 0 | 0 | 0 | 1 | 1 | Military | 0.638174 | 0.528093 | 0.9811 | 0.1667 | 0.9811 | 0.1667 | 0.9811 | 0.1667 | 0.0531 | No | 3.0 | 1.0 | 3.0 | 1.0 | -679.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 |
| 218140 | 352730 | 0 | Revolving loans | F | N | Y | 1 | 67500.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.031329 | -18278 | -359 | -197.0 | -1746 | 1 | 1 | 1 | 1 | 0 | 0 | Cooking staff | 3.0 | 2 | 2 | THURSDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Restaurant | 0.702230 | NaN | 0.9881 | 0.0833 | 0.9881 | 0.0833 | 0.9881 | 0.0833 | 0.0325 | No | 7.0 | 1.0 | 7.0 | 0.0 | -647.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 218981 | 353686 | 0 | Revolving loans | M | N | N | 0 | 57600.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Civil marriage | House / apartment | 0.009334 | -18405 | -419 | -10358.0 | -1931 | 1 | 1 | 1 | 1 | 0 | 0 | Laborers | 2.0 | 2 | 2 | WEDNESDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Housing | 0.564967 | NaN | 0.9717 | 0.0000 | 0.9717 | 0.0000 | 0.9717 | 0.0000 | 0.0027 | No | 0.0 | 0.0 | 0.0 | 0.0 | -208.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 219070 | 353787 | 0 | Revolving loans | F | N | Y | 0 | 90000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Incomplete higher | Married | House / apartment | 0.026392 | -10973 | -496 | -10615.0 | -477 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 2.0 | 2 | 2 | FRIDAY | 19 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.600564 | 0.304672 | 0.9856 | 0.1667 | 0.9856 | 0.1667 | 0.9856 | 0.1667 | 0.1244 | No | 0.0 | 0.0 | 0.0 | 0.0 | -465.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 219075 | 353792 | 0 | Revolving loans | F | N | Y | 0 | 112500.0 | 337500.0 | 16875.0 | NaN | NaN | Working | Secondary / secondary special | Widow | House / apartment | 0.003818 | -23141 | -5088 | -10574.0 | -4053 | 1 | 1 | 1 | 1 | 1 | 0 | Cleaning staff | 1.0 | 2 | 2 | TUESDAY | 3 | 0 | 0 | 0 | 0 | 0 | 0 | School | 0.342313 | NaN | 0.9707 | 0.0833 | 0.9707 | 0.0833 | 0.9707 | 0.0833 | 0.0126 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1138.0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 221204 | 356244 | 0 | Revolving loans | F | N | N | 1 | 90000.0 | 337500.0 | 16875.0 | NaN | NaN | Working | Secondary / secondary special | Married | Municipal apartment | 0.007114 | -13111 | -3350 | -7446.0 | -3519 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 3.0 | 2 | 2 | WEDNESDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Medicine | 0.262936 | 0.339288 | 0.9702 | 0.0417 | 0.9702 | 0.0417 | 0.9702 | 0.0417 | 0.0101 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1484.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 223226 | 358555 | 0 | Revolving loans | F | N | Y | 1 | 90000.0 | 135000.0 | 6750.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.020713 | -12233 | -4818 | -201.0 | -3237 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 3.0 | 3 | 3 | SUNDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.109963 | 0.410103 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 0.0 | 4.0 | 0.0 | -78.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 223502 | 358867 | 0 | Revolving loans | M | N | Y | 0 | 103500.0 | 270000.0 | 13500.0 | NaN | NaN | Commercial associate | Higher education | Single / not married | House / apartment | 0.025164 | -8402 | -402 | -7149.0 | -1060 | 1 | 1 | 0 | 1 | 1 | 0 | Laborers | 1.0 | 2 | 2 | THURSDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.521692 | 0.470456 | 0.9702 | 0.0833 | 0.9702 | 0.0833 | 0.9702 | 0.0833 | 0.0570 | No | 3.0 | 0.0 | 3.0 | 0.0 | -1719.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 1.0 |
| 225994 | 361759 | 0 | Revolving loans | F | N | Y | 1 | 76500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Incomplete higher | Married | House / apartment | 0.008625 | -8401 | -1048 | -2957.0 | -1083 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 3.0 | 2 | 2 | TUESDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.145660 | 0.163426 | 0.9771 | 0.1667 | 0.9772 | 0.1667 | 0.9771 | 0.1667 | 0.0496 | No | 0.0 | 0.0 | 0.0 | 0.0 | -549.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 8.0 |
| 226725 | 362616 | 1 | Revolving loans | F | N | Y | 0 | 76500.0 | 247500.0 | 12375.0 | NaN | NaN | Working | Secondary / secondary special | Civil marriage | House / apartment | 0.028663 | -20072 | -1931 | -6006.0 | -367 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 2.0 | 2 | 2 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.602200 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -794.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 227450 | 363460 | 0 | Revolving loans | M | N | Y | 0 | 202500.0 | 495000.0 | 24750.0 | NaN | NaN | Working | Higher education | Married | House / apartment | 0.020713 | -16190 | -3121 | -1436.0 | -5020 | 1 | 1 | 1 | 1 | 0 | 0 | Managers | 2.0 | 3 | 3 | WEDNESDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.408089 | 0.230159 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1374.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 5.0 |
| 229877 | 366256 | 1 | Revolving loans | M | N | Y | 0 | 202500.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.024610 | -15431 | -486 | -1747.0 | -2113 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 2.0 | 2 | 2 | TUESDAY | 12 | 0 | 0 | 0 | 0 | 1 | 1 | Business Entity Type 3 | 0.366604 | 0.427657 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -188.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 229923 | 366310 | 0 | Revolving loans | F | N | N | 0 | 135000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Higher education | Single / not married | With parents | 0.016612 | -12917 | -1288 | -1961.0 | -1966 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 1.0 | 2 | 2 | TUESDAY | 14 | 0 | 0 | 0 | 0 | 1 | 1 | Self-employed | 0.534857 | 0.154744 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -382.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 |
| 230455 | 366938 | 0 | Revolving loans | M | N | Y | 0 | 135000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Separated | House / apartment | 0.009175 | -16772 | -2257 | -10834.0 | -250 | 1 | 1 | 0 | 1 | 1 | 0 | NaN | 1.0 | 2 | 2 | WEDNESDAY | 16 | 0 | 0 | 0 | 0 | 1 | 1 | Self-employed | 0.769098 | 0.593718 | 0.9796 | 0.1667 | 0.9796 | 0.1667 | 0.9796 | 0.1667 | 0.0522 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1782.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.0 |
| 230484 | 366971 | 0 | Revolving loans | F | N | Y | 1 | 90000.0 | 135000.0 | 6750.0 | NaN | NaN | Working | Higher education | Married | House / apartment | 0.010147 | -12594 | -2357 | -2894.0 | -2723 | 1 | 1 | 1 | 1 | 0 | 0 | Sales staff | 3.0 | 2 | 2 | THURSDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.790072 | 0.746300 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.0 | 2.0 | 0.0 | -351.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 233542 | 370507 | 0 | Revolving loans | F | N | Y | 0 | 99000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Higher education | Single / not married | House / apartment | 0.007305 | -9691 | -1052 | -4103.0 | -2363 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 1.0 | 3 | 3 | SATURDAY | 8 | 0 | 0 | 0 | 1 | 1 | 0 | Business Entity Type 3 | 0.529527 | 0.169429 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1927.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
| 236260 | 373660 | 0 | Revolving loans | M | N | Y | 0 | 90000.0 | 247500.0 | 12375.0 | NaN | NaN | Pensioner | Secondary / secondary special | Married | House / apartment | 0.022625 | -20611 | 365243 | -1888.0 | -1035 | 1 | 0 | 0 | 1 | 1 | 0 | NaN | 2.0 | 2 | 2 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.559014 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -874.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 236624 | 374077 | 0 | Revolving loans | M | N | N | 0 | 135000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.008575 | -9561 | -2206 | -6633.0 | -2206 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 1.0 | 2 | 2 | TUESDAY | 17 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.650118 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -1412.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 1.0 |
| 236974 | 374479 | 0 | Revolving loans | F | N | N | 1 | 225000.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Separated | With parents | 0.020713 | -12869 | -2118 | -2960.0 | -503 | 1 | 1 | 1 | 1 | 1 | 0 | Accountants | 2.0 | 3 | 3 | WEDNESDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Telecom | 0.548710 | NaN | 0.9816 | 0.1667 | 0.9816 | 0.1667 | 0.9816 | 0.1667 | 0.0563 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1367.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 237074 | 374594 | 0 | Revolving loans | M | N | Y | 3 | 243000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.025164 | -13569 | -2811 | -1052.0 | -4434 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 5.0 | 2 | 2 | THURSDAY | 8 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 2 | 0.661696 | 0.798137 | 0.9876 | 0.3333 | 0.9876 | 0.3333 | 0.9876 | 0.3333 | 0.2396 | No | 4.0 | 0.0 | 4.0 | 0.0 | 0.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 |
| 239491 | 377340 | 0 | Revolving loans | F | N | Y | 0 | 180000.0 | 405000.0 | 20250.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.018209 | -18894 | -6134 | -9446.0 | -2392 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 2.0 | 3 | 3 | SATURDAY | 8 | 0 | 0 | 0 | 0 | 1 | 1 | Military | 0.576934 | 0.461482 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.0 | 2.0 | 0.0 | -1482.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 239835 | 377705 | 0 | Revolving loans | F | N | Y | 2 | 225000.0 | 202500.0 | 10125.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Married | House / apartment | 0.005084 | -14050 | -680 | -4525.0 | -4525 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 4.0 | 2 | 2 | SUNDAY | 15 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.486742 | 0.609276 | 0.9841 | 0.2917 | 0.9841 | 0.2917 | 0.9841 | 0.2917 | 0.0877 | No | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 |
| 240223 | 378214 | 0 | Revolving loans | F | N | Y | 0 | 54000.0 | 157500.0 | 7875.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.018634 | -19827 | -11367 | -9770.0 | -2794 | 1 | 1 | 1 | 1 | 0 | 0 | NaN | 2.0 | 2 | 2 | WEDNESDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | School | 0.430334 | 0.132597 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 0.0 | 0.0 | -236.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 242000 | 380194 | 0 | Revolving loans | F | N | Y | 2 | 90000.0 | 247500.0 | 12375.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.006629 | -13436 | -78 | -3787.0 | -5239 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 4.0 | 2 | 2 | TUESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.552606 | NaN | 0.9881 | NaN | 0.9881 | NaN | 0.9881 | NaN | 0.0119 | No | 0.0 | 0.0 | 0.0 | 0.0 | -654.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 242020 | 380214 | 0 | Revolving loans | F | N | N | 0 | 81000.0 | 135000.0 | 6750.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.005313 | -9295 | -1310 | -9295.0 | -1986 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 1.0 | 2 | 2 | SATURDAY | 10 | 0 | 0 | 0 | 1 | 1 | 0 | Business Entity Type 3 | 0.652824 | 0.239226 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -373.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 242307 | 380511 | 0 | Revolving loans | F | N | Y | 0 | 94500.0 | 135000.0 | 6750.0 | NaN | NaN | Pensioner | Secondary / secondary special | Married | House / apartment | 0.014520 | -20940 | 365243 | -6766.0 | -3964 | 1 | 0 | 0 | 1 | 1 | 0 | Cleaning staff | 2.0 | 2 | 2 | SUNDAY | 8 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.673530 | 0.160489 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 | 1.0 | 3.0 | 1.0 | -1462.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 242644 | 380891 | 0 | Revolving loans | M | N | Y | 2 | 112500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.010500 | -10229 | -1468 | -893.0 | -2628 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 4.0 | 3 | 3 | FRIDAY | 10 | 0 | 1 | 1 | 0 | 1 | 1 | Business Entity Type 3 | 0.285898 | 0.374021 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -2295.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 243268 | 381622 | 0 | Revolving loans | F | N | Y | 1 | 292500.0 | 180000.0 | 9000.0 | NaN | NaN | State servant | Higher education | Married | House / apartment | 0.072508 | -13634 | -380 | -6919.0 | -1537 | 1 | 1 | 1 | 1 | 1 | 1 | Core staff | 3.0 | 1 | 1 | SATURDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | School | 0.731164 | 0.321735 | 0.9806 | 0.3333 | 0.9806 | 0.3333 | 0.9806 | 0.3333 | 0.1370 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1837.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 244620 | 383122 | 0 | Revolving loans | M | N | Y | 0 | 157500.0 | 337500.0 | 16875.0 | NaN | NaN | Working | Higher education | Single / not married | Municipal apartment | 0.031329 | -12198 | -4453 | -7440.0 | -4516 | 1 | 1 | 1 | 1 | 1 | 0 | Accountants | 1.0 | 2 | 2 | FRIDAY | 17 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.658556 | 0.051329 | 0.9896 | 0.2500 | 0.9896 | 0.2500 | 0.9896 | 0.2500 | 0.1294 | No | 0.0 | 0.0 | 0.0 | 0.0 | -420.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 244962 | 383538 | 0 | Revolving loans | M | N | Y | 0 | 90000.0 | 225000.0 | 11250.0 | NaN | NaN | Working | Secondary / secondary special | Civil marriage | House / apartment | 0.022625 | -21181 | -2732 | -831.0 | -4084 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 2.0 | 2 | 2 | SUNDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 1 | 0.667899 | 0.520898 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.0 | 0.0 | 5.0 | 0.0 | -2361.0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 245542 | 384157 | 0 | Revolving loans | F | N | Y | 0 | 54000.0 | 180000.0 | 9000.0 | NaN | NaN | Pensioner | Higher education | Married | House / apartment | 0.000533 | -22259 | 365243 | -3862.0 | -1405 | 1 | 0 | 0 | 1 | 0 | 0 | NaN | 2.0 | 3 | 3 | TUESDAY | 12 | 1 | 0 | 0 | 0 | 0 | 0 | XNA | 0.583607 | 0.651260 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.0 | 2.0 | 0.0 | -952.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 246989 | 385808 | 0 | Revolving loans | F | N | Y | 0 | 67500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.025164 | -8044 | -846 | -545.0 | -720 | 1 | 1 | 0 | 1 | 1 | 0 | Laborers | 1.0 | 2 | 2 | THURSDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.165183 | 0.581484 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 1.0 | 2.0 | 1.0 | -927.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 2.0 |
| 247473 | 386374 | 0 | Revolving loans | F | N | N | 0 | 112500.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Higher education | Married | With parents | 0.009175 | -8040 | -1162 | -2638.0 | -511 | 1 | 1 | 0 | 1 | 1 | 0 | NaN | 2.0 | 2 | 2 | WEDNESDAY | 11 | 0 | 1 | 1 | 0 | 1 | 1 | Other | 0.649412 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 248749 | 387780 | 0 | Revolving loans | F | N | Y | 1 | 103500.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Civil marriage | House / apartment | 0.020246 | -12675 | -3486 | -795.0 | -4100 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 3.0 | 3 | 3 | TUESDAY | 15 | 0 | 0 | 0 | 0 | 0 | 0 | Construction | 0.282548 | 0.701696 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -109.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 249109 | 388225 | 0 | Revolving loans | M | N | N | 0 | 112500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Civil marriage | House / apartment | 0.010147 | -9584 | -1477 | -9393.0 | -2247 | 1 | 1 | 1 | 1 | 0 | 0 | Laborers | 2.0 | 2 | 2 | WEDNESDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.722314 | 0.771362 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.0 | 2.0 | 0.0 | -1742.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.0 |
| 249616 | 388813 | 1 | Revolving loans | F | N | N | 0 | 90000.0 | 157500.0 | 7875.0 | NaN | NaN | Pensioner | Secondary / secondary special | Civil marriage | House / apartment | 0.010032 | -21440 | 365243 | -8945.0 | -4756 | 1 | 0 | 0 | 1 | 1 | 0 | NaN | 2.0 | 2 | 2 | MONDAY | 6 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.672794 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 0.0 | 4.0 | 0.0 | -1824.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 253126 | 392897 | 1 | Revolving loans | M | N | Y | 1 | 157500.0 | 337500.0 | 16875.0 | NaN | NaN | Commercial associate | Higher education | Married | House / apartment | 0.010147 | -11233 | -3189 | -299.0 | -3883 | 1 | 1 | 1 | 1 | 1 | 0 | Managers | 3.0 | 2 | 2 | SUNDAY | 13 | 0 | 0 | 0 | 0 | 1 | 1 | Trade: type 2 | 0.349855 | 0.092013 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 12.0 | 0.0 | 10.0 | 0.0 | -1608.0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 |
| 254451 | 394441 | 0 | Revolving loans | F | N | Y | 0 | 90000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.020246 | -18950 | -5789 | -10066.0 | -2499 | 1 | 1 | 1 | 1 | 1 | 0 | Private service staff | 2.0 | 3 | 3 | THURSDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | Services | 0.342535 | 0.595456 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.0 | 2.0 | 0.0 | -1464.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 255049 | 395122 | 0 | Revolving loans | M | N | N | 3 | 135000.0 | 225000.0 | 11250.0 | NaN | NaN | Working | Secondary / secondary special | Civil marriage | House / apartment | 0.031329 | -13455 | -546 | -330.0 | -2803 | 1 | 1 | 1 | 1 | 0 | 0 | Laborers | 5.0 | 2 | 2 | SUNDAY | 13 | 0 | 0 | 0 | 1 | 1 | 1 | Transport: type 4 | 0.577937 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -982.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 256395 | 396696 | 0 | Revolving loans | M | N | Y | 2 | 96750.0 | 270000.0 | 13500.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Married | House / apartment | 0.024610 | -13634 | -3505 | -4146.0 | -4153 | 1 | 1 | 1 | 1 | 0 | 0 | NaN | 4.0 | 2 | 2 | THURSDAY | 13 | 0 | 0 | 0 | 0 | 1 | 1 | Business Entity Type 3 | 0.636122 | 0.135951 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -749.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 |
| 256843 | 397231 | 0 | Revolving loans | F | N | N | 0 | 81000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | With parents | 0.005313 | -10364 | -539 | -9820.0 | -2835 | 1 | 1 | 0 | 1 | 1 | 0 | NaN | 1.0 | 2 | 2 | THURSDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.490660 | 0.617826 | 0.9801 | 0.1667 | 0.9801 | 0.1667 | 0.9801 | 0.1667 | 0.0876 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1869.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 |
| 257017 | 397421 | 0 | Revolving loans | F | N | Y | 3 | 67500.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Married | Municipal apartment | 0.005002 | -13245 | -3080 | -7.0 | -4333 | 1 | 1 | 1 | 1 | 1 | 1 | Sales staff | 5.0 | 3 | 3 | MONDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.431240 | 0.504681 | 0.9791 | 0.0417 | 0.9791 | 0.0417 | 0.9791 | 0.0417 | 0.0056 | No | 0.0 | 0.0 | 0.0 | 0.0 | -362.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 257118 | 397542 | 0 | Revolving loans | M | N | Y | 3 | 112500.0 | 337500.0 | 16875.0 | NaN | NaN | State servant | Higher education | Married | House / apartment | 0.019101 | -16359 | -3137 | -5397.0 | -5426 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 5.0 | 2 | 2 | WEDNESDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Security Ministries | 0.485710 | 0.652897 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6.0 | 1.0 | 6.0 | 0.0 | -2118.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 257405 | 397874 | 0 | Revolving loans | M | N | Y | 0 | 112500.0 | 225000.0 | 11250.0 | NaN | NaN | Working | Secondary / secondary special | Married | With parents | 0.007120 | -11404 | -3539 | -9806.0 | -4032 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 2.0 | 2 | 2 | WEDNESDAY | 12 | 0 | 0 | 0 | 1 | 1 | 0 | Business Entity Type 2 | 0.751028 | 0.540654 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -1707.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 259788 | 400636 | 0 | Revolving loans | M | N | N | 1 | 112500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Separated | House / apartment | 0.010147 | -14023 | -823 | -913.0 | -4385 | 1 | 1 | 0 | 1 | 0 | 0 | Laborers | 2.0 | 2 | 2 | SATURDAY | 17 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.604298 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 260704 | 401702 | 1 | Revolving loans | F | N | Y | 0 | 67500.0 | 135000.0 | 6750.0 | NaN | NaN | Working | Incomplete higher | Married | House / apartment | 0.025164 | -8500 | -1367 | -711.0 | -1177 | 1 | 1 | 0 | 1 | 0 | 0 | Core staff | 2.0 | 2 | 2 | FRIDAY | 15 | 0 | 0 | 0 | 0 | 0 | 0 | Kindergarten | 0.372580 | 0.317032 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1308.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 262052 | 403332 | 0 | Revolving loans | M | N | Y | 0 | 81000.0 | 225000.0 | 11250.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.028663 | -16424 | -3206 | -7575.0 | -4421 | 1 | 1 | 1 | 1 | 1 | 1 | Medicine staff | 2.0 | 2 | 2 | WEDNESDAY | 8 | 0 | 0 | 0 | 0 | 1 | 1 | Medicine | 0.604920 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -428.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 262572 | 404012 | 0 | Revolving loans | M | N | Y | 0 | 135000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | With parents | 0.026392 | -9525 | -1277 | -4330.0 | -2211 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 1.0 | 2 | 2 | FRIDAY | 17 | 0 | 0 | 0 | 0 | 1 | 1 | Business Entity Type 3 | 0.398412 | 0.324891 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1773.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 262657 | 404102 | 0 | Revolving loans | F | N | Y | 0 | 202500.0 | 585000.0 | 29250.0 | NaN | NaN | State servant | Higher education | Married | House / apartment | 0.022625 | -14688 | -1046 | -2874.0 | -4268 | 1 | 1 | 1 | 1 | 1 | 1 | Accountants | 2.0 | 2 | 2 | TUESDAY | 18 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.321549 | 0.739412 | 0.9791 | 0.1667 | 0.9791 | 0.1667 | 0.9791 | 0.1667 | 0.0582 | No | 0.0 | 0.0 | 0.0 | 0.0 | -365.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 262853 | 404359 | 0 | Revolving loans | F | N | Y | 0 | 112500.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Higher education | Separated | House / apartment | 0.046220 | -19556 | -2027 | -3611.0 | -2668 | 1 | 1 | 1 | 1 | 1 | 0 | Cleaning staff | 1.0 | 1 | 1 | THURSDAY | 10 | 0 | 1 | 1 | 0 | 1 | 1 | Business Entity Type 3 | 0.708239 | 0.547810 | 0.9757 | 0.1250 | 0.9757 | 0.1250 | 0.9757 | 0.1250 | 0.0488 | No | 1.0 | 1.0 | 1.0 | 0.0 | -1512.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 263035 | 404580 | 0 | Revolving loans | M | N | Y | 0 | 382500.0 | 1350000.0 | 67500.0 | NaN | NaN | Working | Higher education | Married | House / apartment | 0.007330 | -17474 | -2012 | -1009.0 | -1029 | 1 | 1 | 0 | 1 | 0 | 0 | Managers | 2.0 | 2 | 2 | WEDNESDAY | 15 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.726001 | 0.673830 | 0.9945 | 0.3750 | 0.9945 | 0.3750 | 0.9945 | 0.3750 | 0.1703 | No | 0.0 | 0.0 | 0.0 | 0.0 | -145.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 263213 | 404774 | 0 | Revolving loans | M | N | N | 1 | 180000.0 | 315000.0 | 15750.0 | NaN | NaN | Working | Higher education | Married | Rented apartment | 0.035792 | -12279 | -2565 | -6341.0 | -4268 | 1 | 1 | 1 | 1 | 1 | 0 | Drivers | 3.0 | 2 | 2 | TUESDAY | 9 | 0 | 0 | 0 | 1 | 1 | 0 | Transport: type 3 | 0.299441 | 0.372334 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -317.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 3.0 |
| 267302 | 409698 | 0 | Revolving loans | F | N | Y | 0 | 67500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Widow | House / apartment | 0.008474 | -18120 | -915 | -8162.0 | -919 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 1.0 | 2 | 2 | FRIDAY | 13 | 0 | 0 | 0 | 0 | 1 | 1 | Business Entity Type 3 | 0.669807 | 0.651260 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -628.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 1.0 |
| 267335 | 409733 | 0 | Revolving loans | F | N | N | 0 | 135000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | With parents | 0.028663 | -8869 | -1601 | -1379.0 | -1533 | 1 | 1 | 0 | 1 | 0 | 0 | Waiters/barmen staff | 1.0 | 2 | 2 | THURSDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Restaurant | 0.565440 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 9.0 | 0.0 | 9.0 | 0.0 | 0.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 267367 | 409768 | 0 | Revolving loans | F | N | Y | 0 | 112500.0 | 135000.0 | 6750.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.007114 | -15667 | -2028 | -5495.0 | -5498 | 1 | 1 | 1 | 1 | 0 | 0 | Core staff | 2.0 | 2 | 2 | FRIDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Government | 0.527173 | 0.706205 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 2.0 | 2.0 | 2.0 | -715.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 269291 | 412079 | 0 | Revolving loans | M | N | Y | 1 | 67500.0 | 135000.0 | 6750.0 | NaN | NaN | Working | Secondary / secondary special | Civil marriage | House / apartment | 0.009657 | -15055 | -3476 | -8984.0 | -4158 | 1 | 1 | 1 | 1 | 0 | 0 | NaN | 3.0 | 2 | 2 | FRIDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.475808 | 0.382502 | 0.9747 | 0.0417 | 0.9747 | 0.0417 | 0.9747 | 0.0417 | 0.0097 | No | 1.0 | 0.0 | 1.0 | 0.0 | -442.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 270616 | 413674 | 1 | Revolving loans | F | N | Y | 0 | 99000.0 | 337500.0 | 16875.0 | NaN | NaN | Pensioner | Secondary / secondary special | Married | House / apartment | 0.030755 | -21827 | 365243 | -3582.0 | -4737 | 1 | 0 | 0 | 1 | 1 | 0 | NaN | 2.0 | 2 | 2 | WEDNESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.647882 | 0.528093 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1915.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 271074 | 414243 | 0 | Revolving loans | F | N | Y | 0 | 157500.0 | 810000.0 | 40500.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | Municipal apartment | 0.024610 | -14617 | -588 | -8712.0 | -4499 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 1.0 | 2 | 2 | TUESDAY | 17 | 0 | 0 | 0 | 0 | 0 | 0 | Transport: type 4 | 0.469714 | 0.535276 | 0.9598 | 0.1250 | 0.9598 | 0.1250 | 0.9598 | 0.1250 | 0.0353 | No | 4.0 | 0.0 | 4.0 | 0.0 | -995.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 271339 | 414555 | 0 | Revolving loans | F | N | Y | 0 | 112500.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.018850 | -13052 | -1837 | -3207.0 | -4613 | 1 | 1 | 1 | 1 | 1 | 1 | High skill tech staff | 2.0 | 2 | 2 | TUESDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Housing | 0.619842 | 0.631355 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -629.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 |
| 271611 | 414876 | 0 | Revolving loans | M | N | Y | 0 | 135000.0 | 157500.0 | 7875.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.010966 | -14876 | -1577 | -1665.0 | -3422 | 1 | 1 | 1 | 1 | 0 | 0 | Drivers | 2.0 | 2 | 2 | THURSDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.521599 | 0.511892 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 271754 | 415039 | 0 | Revolving loans | M | N | Y | 1 | 121500.0 | 225000.0 | 11250.0 | NaN | NaN | Working | Secondary / secondary special | Married | With parents | 0.008474 | -9939 | -886 | -4801.0 | -2283 | 1 | 1 | 0 | 1 | 1 | 0 | Core staff | 3.0 | 2 | 2 | THURSDAY | 15 | 0 | 0 | 0 | 1 | 1 | 0 | Business Entity Type 1 | 0.590451 | 0.270707 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -660.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 272242 | 415625 | 0 | Revolving loans | M | N | N | 0 | 126000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.007120 | -13322 | -1632 | -776.0 | -4690 | 1 | 1 | 1 | 1 | 1 | 0 | Drivers | 1.0 | 2 | 2 | WEDNESDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Industry: type 3 | 0.606164 | NaN | 0.9737 | 0.0417 | 0.9737 | 0.0417 | 0.9737 | 0.0417 | 0.0053 | No | 0.0 | 0.0 | 0.0 | 0.0 | -897.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 272252 | 415635 | 0 | Revolving loans | F | N | Y | 0 | 90000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Separated | House / apartment | 0.010147 | -14704 | -831 | -1999.0 | -5130 | 1 | 1 | 0 | 1 | 0 | 0 | Sales staff | 1.0 | 2 | 2 | MONDAY | 11 | 0 | 0 | 0 | 0 | 0 | 0 | Trade: type 7 | 0.030760 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -669.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 2.0 |
| 273229 | 416712 | 0 | Revolving loans | F | N | Y | 1 | 112500.0 | 180000.0 | 9000.0 | NaN | NaN | State servant | Secondary / secondary special | Married | House / apartment | 0.002042 | -15098 | -6407 | -1033.0 | -6085 | 1 | 1 | 1 | 1 | 0 | 0 | Medicine staff | 3.0 | 3 | 3 | SATURDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Medicine | 0.506895 | 0.672243 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -815.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 273757 | 417309 | 0 | Revolving loans | F | N | Y | 0 | 65250.0 | 202500.0 | 10125.0 | NaN | NaN | Pensioner | Secondary / secondary special | Married | Municipal apartment | 0.010006 | -21538 | 365243 | -3303.0 | -2366 | 1 | 0 | 0 | 1 | 1 | 0 | NaN | 2.0 | 2 | 2 | TUESDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | NaN | NaN | 0.9786 | 0.0417 | 0.9786 | 0.0417 | 0.9786 | 0.0417 | 0.0074 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1910.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 275284 | 419023 | 0 | Revolving loans | M | N | Y | 0 | 90000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.007330 | -18394 | -11426 | -10186.0 | -1933 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 1.0 | 2 | 2 | FRIDAY | 18 | 0 | 0 | 0 | 0 | 0 | 0 | Culture | 0.721388 | NaN | 0.9821 | 0.3333 | 0.9821 | 0.3333 | 0.9821 | 0.3333 | 0.2422 | No | 0.0 | 0.0 | 0.0 | 0.0 | -399.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 1.0 |
| 275429 | 419205 | 0 | Revolving loans | F | N | Y | 1 | 112500.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.010147 | -8120 | -731 | -7506.0 | -341 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 2.0 | 2 | 2 | TUESDAY | 16 | 0 | 0 | 0 | 0 | 1 | 1 | Self-employed | 0.109114 | 0.210350 | 0.9861 | 0.0833 | 0.9861 | 0.0833 | 0.9861 | 0.0833 | 0.0210 | No | 0.0 | 0.0 | 0.0 | 0.0 | -311.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 |
| 276692 | 420659 | 0 | Revolving loans | M | N | Y | 0 | 112500.0 | 247500.0 | 12375.0 | NaN | NaN | Pensioner | Higher education | Married | House / apartment | 0.018209 | -22496 | 365243 | -4018.0 | -4087 | 1 | 0 | 0 | 1 | 1 | 0 | NaN | 2.0 | 3 | 3 | TUESDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.527037 | 0.529890 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -204.0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 277969 | 422096 | 0 | Revolving loans | F | N | Y | 1 | 90000.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.007020 | -7950 | -1068 | -116.0 | -577 | 1 | 1 | 1 | 1 | 0 | 0 | NaN | 3.0 | 2 | 2 | SATURDAY | 14 | 0 | 0 | 0 | 1 | 1 | 1 | Other | 0.223138 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 8.0 | 0.0 | 8.0 | 0.0 | 0.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 278747 | 422928 | 0 | Revolving loans | M | N | Y | 1 | 117000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.009175 | -12462 | -1203 | -3084.0 | -1610 | 1 | 1 | 0 | 1 | 1 | 1 | Laborers | 3.0 | 2 | 2 | SATURDAY | 17 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.630480 | 0.560284 | 0.9851 | 0.3333 | 0.9851 | 0.3333 | 0.9851 | 0.3333 | 0.1353 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1448.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 | 7.0 |
| 279583 | 423902 | 0 | Revolving loans | F | N | Y | 1 | 45000.0 | 135000.0 | 6750.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.010966 | -9903 | -185 | -2559.0 | -1187 | 1 | 1 | 1 | 1 | 1 | 1 | Managers | 2.0 | 2 | 2 | TUESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Postal | 0.609775 | NaN | 0.9806 | 0.1667 | 0.9806 | 0.1667 | 0.9806 | 0.1667 | 0.0482 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1628.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 279997 | 424410 | 0 | Revolving loans | F | N | Y | 1 | 112500.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Separated | House / apartment | 0.020246 | -13598 | -384 | -247.0 | -797 | 1 | 1 | 1 | 1 | 1 | 0 | Sales staff | 2.0 | 3 | 3 | THURSDAY | 7 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.424399 | 0.377404 | 0.9717 | NaN | 0.9717 | NaN | 0.9717 | NaN | 0.0069 | No | 0.0 | 0.0 | 0.0 | 0.0 | -442.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 281050 | 425623 | 0 | Revolving loans | M | N | Y | 0 | 225000.0 | 540000.0 | 27000.0 | NaN | NaN | Working | Higher education | Married | House / apartment | 0.018634 | -19645 | -621 | -8405.0 | -845 | 1 | 1 | 0 | 1 | 1 | 0 | Managers | 2.0 | 2 | 2 | MONDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | Construction | 0.372064 | 0.676993 | 0.9871 | NaN | 0.9871 | NaN | 0.9871 | NaN | 0.1387 | No | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 281653 | 426277 | 0 | Revolving loans | F | N | Y | 0 | 45000.0 | 135000.0 | 6750.0 | NaN | NaN | State servant | Secondary / secondary special | Married | House / apartment | 0.025164 | -18860 | -7885 | -9087.0 | -2408 | 1 | 1 | 1 | 1 | 0 | 0 | Cooking staff | 2.0 | 2 | 2 | THURSDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Kindergarten | 0.299881 | 0.680139 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 1.0 | 1.0 | 0.0 | -187.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 282002 | 426638 | 0 | Revolving loans | M | N | Y | 1 | 121500.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.016612 | -15706 | -2426 | -2654.0 | -4261 | 1 | 1 | 1 | 1 | 1 | 0 | Security staff | 3.0 | 2 | 2 | SATURDAY | 12 | 0 | 1 | 1 | 0 | 1 | 1 | Security | 0.637720 | 0.556727 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.0 | 2.0 | 0.0 | -1794.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 282270 | 426914 | 0 | Revolving loans | F | N | Y | 0 | 126000.0 | 382500.0 | 19125.0 | NaN | NaN | Commercial associate | Higher education | Married | Municipal apartment | 0.010006 | -10156 | -1470 | -3418.0 | -1773 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 2.0 | 2 | 2 | SUNDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.488638 | 0.504681 | 0.9891 | 0.1667 | 0.9891 | 0.1667 | 0.9891 | 0.1667 | 0.0480 | No | 5.0 | 0.0 | 5.0 | 0.0 | -743.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 282847 | 427592 | 0 | Revolving loans | F | N | N | 0 | 112500.0 | 337500.0 | 16875.0 | NaN | NaN | State servant | Higher education | Married | House / apartment | 0.001276 | -10978 | -1791 | -2216.0 | -652 | 1 | 1 | 0 | 1 | 0 | 0 | Sales staff | 2.0 | 2 | 2 | WEDNESDAY | 8 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.322456 | 0.641368 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6.0 | 0.0 | 6.0 | 0.0 | -1147.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 283159 | 427953 | 0 | Revolving loans | F | N | Y | 0 | 90000.0 | 202500.0 | 10125.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Single / not married | With parents | 0.024610 | -7835 | -602 | -2571.0 | -500 | 1 | 1 | 0 | 1 | 1 | 0 | Cooking staff | 1.0 | 2 | 2 | FRIDAY | 16 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.370166 | NaN | 0.9677 | 0.0417 | 0.9677 | 0.0417 | 0.9677 | 0.0417 | 0.0070 | No | 4.0 | 2.0 | 4.0 | 2.0 | -98.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 285269 | 430381 | 0 | Revolving loans | M | N | Y | 1 | 180000.0 | 270000.0 | 13500.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Married | House / apartment | 0.010500 | -13706 | -991 | -9824.0 | -3904 | 1 | 1 | 1 | 1 | 1 | 0 | Managers | 3.0 | 3 | 3 | SUNDAY | 15 | 1 | 1 | 0 | 1 | 1 | 0 | Construction | 0.673492 | NaN | 0.9851 | 0.1667 | 0.9851 | 0.1667 | 0.9851 | 0.1667 | 0.0434 | No | 0.0 | 0.0 | 0.0 | 0.0 | -526.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 287092 | 432536 | 0 | Revolving loans | M | N | Y | 0 | 90000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.022625 | -21339 | -13031 | -6244.0 | -4646 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 2.0 | 2 | 2 | TUESDAY | 17 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.481554 | NaN | 0.9811 | 0.3333 | 0.9811 | 0.3333 | 0.9811 | 0.3333 | 0.1033 | No | 2.0 | 0.0 | 2.0 | 0.0 | -1475.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 287347 | 432847 | 0 | Revolving loans | F | N | N | 1 | 90000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Married | Rented apartment | 0.009175 | -11099 | -2761 | -4537.0 | -2377 | 1 | 1 | 0 | 1 | 1 | 0 | Sales staff | 3.0 | 2 | 2 | SUNDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.725304 | NaN | 0.9831 | 0.1667 | 0.9831 | 0.1667 | 0.9831 | 0.1667 | 0.1110 | No | 0.0 | 0.0 | 0.0 | 0.0 | -216.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 289793 | 435732 | 0 | Revolving loans | F | N | Y | 2 | 150750.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Civil marriage | House / apartment | 0.030755 | -13541 | -888 | -2876.0 | -4256 | 1 | 1 | 1 | 1 | 1 | 1 | NaN | 4.0 | 2 | 2 | MONDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.475399 | 0.312365 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 0.0 | 1.0 | 0.0 | -1362.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 291372 | 437555 | 0 | Revolving loans | F | N | N | 2 | 99000.0 | 135000.0 | 6750.0 | NaN | NaN | Working | Incomplete higher | Married | With parents | 0.008474 | -14082 | -313 | -6914.0 | -3354 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 4.0 | 2 | 2 | WEDNESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.686397 | 0.643026 | 0.9826 | 0.0833 | 0.9826 | 0.0833 | 0.9826 | 0.0833 | 0.0266 | No | 0.0 | 0.0 | 0.0 | 0.0 | -2109.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 291384 | 437568 | 0 | Revolving loans | M | N | N | 0 | 315000.0 | 675000.0 | 33750.0 | NaN | NaN | Working | Secondary / secondary special | Married | Municipal apartment | 0.020713 | -12970 | -5181 | -562.0 | -4210 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 2.0 | 3 | 3 | FRIDAY | 6 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.034158 | 0.551381 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1184.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 292040 | 438322 | 0 | Revolving loans | F | N | N | 0 | 90000.0 | 270000.0 | 13500.0 | NaN | NaN | Commercial associate | Higher education | Single / not married | House / apartment | 0.026392 | -9108 | -577 | -3872.0 | -1773 | 1 | 1 | 0 | 1 | 1 | 0 | NaN | 1.0 | 2 | 2 | FRIDAY | 18 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.692772 | NaN | 0.9886 | 0.3750 | 0.9886 | 0.3750 | 0.9886 | 0.3750 | 0.2173 | No | 0.0 | 0.0 | 0.0 | 0.0 | -314.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 |
| 292120 | 438413 | 0 | Revolving loans | F | N | Y | 2 | 76500.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Secondary / secondary special | Single / not married | House / apartment | 0.005002 | -14192 | -1233 | -6516.0 | -3316 | 1 | 1 | 1 | 1 | 0 | 0 | NaN | 3.0 | 3 | 3 | TUESDAY | 16 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.772014 | 0.772631 | 0.9811 | 0.1667 | 0.9811 | 0.1667 | 0.9811 | 0.1667 | 0.0562 | No | 0.0 | 0.0 | 0.0 | 0.0 | -3380.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 |
| 294136 | 440763 | 0 | Revolving loans | F | N | N | 2 | 135000.0 | 180000.0 | 9000.0 | NaN | NaN | State servant | Incomplete higher | Married | Rented apartment | 0.008625 | -11124 | -805 | -655.0 | -2234 | 1 | 1 | 1 | 1 | 0 | 0 | High skill tech staff | 4.0 | 2 | 2 | FRIDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Military | 0.570468 | 0.252599 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -491.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 295295 | 442115 | 0 | Revolving loans | F | N | N | 1 | 90000.0 | 225000.0 | 11250.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.009175 | -10909 | -2201 | -4552.0 | -3571 | 1 | 1 | 1 | 1 | 1 | 0 | Private service staff | 3.0 | 2 | 2 | SATURDAY | 16 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.673338 | 0.602386 | 0.9881 | 0.3333 | 0.9881 | 0.3333 | 0.9881 | 0.3333 | 0.0608 | No | 6.0 | 0.0 | 6.0 | 0.0 | -598.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 |
| 298004 | 445250 | 0 | Revolving loans | M | N | Y | 2 | 90000.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.024610 | -11771 | -2479 | -3872.0 | -3913 | 1 | 1 | 0 | 1 | 0 | 0 | Laborers | 4.0 | 2 | 2 | WEDNESDAY | 9 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 2 | 0.591392 | NaN | 0.9861 | 0.1667 | 0.9861 | 0.1667 | 0.9861 | 0.1667 | 0.0507 | No | 1.0 | 0.0 | 1.0 | 0.0 | -161.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 300107 | 447675 | 0 | Revolving loans | F | N | Y | 1 | 405000.0 | 810000.0 | 40500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.010006 | -16267 | -4206 | -5283.0 | -3632 | 1 | 1 | 1 | 1 | 1 | 1 | Drivers | 3.0 | 2 | 2 | WEDNESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.690557 | 0.120641 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -532.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 302923 | 450959 | 0 | Revolving loans | F | N | Y | 0 | 126000.0 | 337500.0 | 16875.0 | NaN | NaN | Working | Secondary / secondary special | Widow | House / apartment | 0.020713 | -23162 | -15871 | -2388.0 | -4284 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 1.0 | 3 | 3 | WEDNESDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 2 | 0.635742 | 0.089631 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -1614.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 303603 | 451760 | 0 | Revolving loans | F | N | Y | 0 | 135000.0 | 135000.0 | 6750.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Civil marriage | Municipal apartment | 0.009175 | -11236 | -2034 | -594.0 | -2157 | 1 | 1 | 1 | 1 | 0 | 0 | High skill tech staff | 2.0 | 2 | 2 | SUNDAY | 17 | 0 | 0 | 0 | 0 | 0 | 0 | University | 0.703107 | 0.347418 | 0.9851 | 0.3333 | 0.9851 | 0.3333 | 0.9851 | 0.3333 | 0.0893 | No | 0.0 | 0.0 | 0.0 | 0.0 | -757.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 303621 | 451781 | 0 | Revolving loans | F | N | Y | 0 | 112500.0 | 180000.0 | 9000.0 | NaN | NaN | Working | Incomplete higher | Civil marriage | House / apartment | 0.007330 | -7836 | -434 | -5474.0 | -492 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 2.0 | 2 | 2 | THURSDAY | 13 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 3 | 0.475922 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.0 | 2.0 | 0.0 | -618.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 304621 | 452926 | 0 | Revolving loans | F | N | Y | 0 | 90000.0 | 247500.0 | 12375.0 | NaN | NaN | Pensioner | Secondary / secondary special | Widow | House / apartment | 0.006008 | -20728 | 365243 | -10861.0 | -4110 | 1 | 0 | 0 | 1 | 1 | 0 | NaN | 1.0 | 2 | 2 | FRIDAY | 12 | 0 | 0 | 0 | 0 | 0 | 0 | XNA | 0.624966 | 0.511892 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -562.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 3.0 |
| 304678 | 452992 | 0 | Revolving loans | F | N | Y | 2 | 67500.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Higher education | Married | House / apartment | 0.025164 | -11313 | -1470 | -1188.0 | -3523 | 1 | 1 | 1 | 1 | 0 | 0 | Sales staff | 4.0 | 2 | 2 | WEDNESDAY | 11 | 0 | 0 | 0 | 1 | 0 | 1 | Business Entity Type 3 | 0.449139 | 0.574447 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 | 1.0 | 3.0 | 0.0 | -777.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 304784 | 453120 | 0 | Revolving loans | M | N | Y | 1 | 112500.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Secondary / secondary special | Married | House / apartment | 0.028663 | -16870 | -825 | -1303.0 | -427 | 1 | 1 | 1 | 1 | 1 | 0 | Drivers | 3.0 | 2 | 2 | THURSDAY | 10 | 0 | 0 | 0 | 1 | 1 | 1 | Self-employed | 0.441976 | 0.459690 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 | 1.0 | 3.0 | 1.0 | 0.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 305833 | 454341 | 0 | Revolving loans | F | N | Y | 0 | 126000.0 | 270000.0 | 13500.0 | NaN | NaN | Working | Incomplete higher | Single / not married | With parents | 0.015221 | -10440 | -3444 | -4452.0 | -3096 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 1.0 | 2 | 2 | THURSDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.480353 | 0.698668 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 0.0 | 2.0 | 0.0 | -163.0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 |
| 306126 | 454683 | 0 | Revolving loans | M | N | Y | 0 | 135000.0 | 270000.0 | 13500.0 | NaN | NaN | Commercial associate | Secondary / secondary special | Married | Municipal apartment | 0.046220 | -15406 | -427 | -8924.0 | -4101 | 1 | 1 | 1 | 1 | 1 | 0 | NaN | 2.0 | 1 | 1 | THURSDAY | 16 | 0 | 1 | 1 | 0 | 1 | 1 | Security | 0.675154 | 0.321735 | 0.9757 | 0.1250 | 0.9757 | 0.1250 | 0.9757 | 0.1250 | 0.0514 | No | 0.0 | 0.0 | 0.0 | 0.0 | -188.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 306273 | 454852 | 0 | Revolving loans | M | N | Y | 2 | 67500.0 | 202500.0 | 10125.0 | NaN | NaN | Working | Higher education | Married | House / apartment | 0.020713 | -14079 | -1648 | -2492.0 | -4905 | 1 | 1 | 1 | 1 | 1 | 0 | Laborers | 4.0 | 3 | 3 | THURSDAY | 6 | 0 | 0 | 0 | 0 | 0 | 0 | Self-employed | 0.608328 | NaN | 0.9752 | 0.1667 | 0.9752 | 0.1667 | 0.9752 | 0.1667 | 0.0602 | No | 0.0 | 0.0 | 0.0 | 0.0 | -1154.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 |
In [18]:
# checking for outliers for of AMT_GOODS_PRICE column and boxplot
print(round(app_df['AMT_GOODS_PRICE'].describe(percentiles=[0.1,0.25,0.5,0.75,0.99]),2))
sns.boxplot(app_df['AMT_GOODS_PRICE'])
count 307233.00 mean 538396.21 std 369446.46 min 40500.00 10% 180000.00 25% 238500.00 50% 450000.00 75% 679500.00 99% 1800000.00 max 4050000.00 Name: AMT_GOODS_PRICE, dtype: float64
Out[18]:
<Axes: ylabel='AMT_GOODS_PRICE'>
In [19]:
# As compared to AMT_ANNUITY column here also we can find a large number of outliersabs
# and the 99th percentile is very far from the max value
# So we can replace the null values with median
In [20]:
# checking for maximum repeated value in NAME_TYPE_SUITE
print(app_df['NAME_TYPE_SUITE'].value_counts())
print('most repeated word :',app_df['NAME_TYPE_SUITE'].mode()[0])
NAME_TYPE_SUITE Unaccompanied 248526 Family 40149 Spouse, partner 11370 Children 3267 Other_B 1770 Other_A 866 Group of people 271 Name: count, dtype: int64 most repeated word : Unaccompanied
In [21]:
# Here we can clearly see that the most repeated word is 'Unaccompanied'
# Since the total number of 'Unaccompanied' is more than 6 times the second most repeated value, we can replace the null columns with 'Unaccompanied'
In [22]:
# checking AMT_GOODS_PRICE column
app_df[app_df['CNT_FAM_MEMBERS'].isnull()]
Out[22]:
| SK_ID_CURR | TARGET | NAME_CONTRACT_TYPE | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | NAME_TYPE_SUITE | NAME_INCOME_TYPE | NAME_EDUCATION_TYPE | NAME_FAMILY_STATUS | NAME_HOUSING_TYPE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | OCCUPATION_TYPE | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | WEEKDAY_APPR_PROCESS_START | HOUR_APPR_PROCESS_START | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | ORGANIZATION_TYPE | EXT_SOURCE_2 | EXT_SOURCE_3 | YEARS_BEGINEXPLUATATION_AVG | FLOORSMAX_AVG | YEARS_BEGINEXPLUATATION_MODE | FLOORSMAX_MODE | YEARS_BEGINEXPLUATATION_MEDI | FLOORSMAX_MEDI | TOTALAREA_MODE | EMERGENCYSTATE_MODE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 41982 | 148605 | 0 | Revolving loans | M | N | Y | 0 | 450000.0 | 675000.0 | 33750.0 | NaN | NaN | Commercial associate | Lower secondary | Unknown | Municipal apartment | 0.015221 | -12396 | -1161 | -3265.0 | -4489 | 1 | 1 | 1 | 1 | 1 | 0 | Managers | NaN | 2 | 2 | THURSDAY | 15 | 0 | 1 | 1 | 0 | 1 | 1 | Insurance | 0.700618 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 | 0.0 | 3.0 | 0.0 | -876.0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 187348 | 317181 | 0 | Revolving loans | F | N | Y | 0 | 202500.0 | 585000.0 | 29250.0 | NaN | NaN | Commercial associate | Higher education | Unknown | House / apartment | 0.031329 | -12844 | -232 | -1597.0 | -1571 | 1 | 1 | 0 | 1 | 0 | 0 | Accountants | NaN | 2 | 2 | FRIDAY | 14 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 2 | 0.645168 | 0.670652 | 0.997 | 0.375 | 0.997 | 0.375 | 0.997 | 0.375 | 0.0791 | No | 1.0 | 0.0 | 1.0 | 0.0 | -654.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
In [23]:
# checking for outliers using statistical summary of CNT_FAM_MEMBERS column and boxplot
print(round(app_df['CNT_FAM_MEMBERS'].describe(percentiles=[0.1,0.25,0.5,0.75,0.99]),2))
sns.boxplot(app_df['CNT_FAM_MEMBERS'])
count 307509.00 mean 2.15 std 0.91 min 1.00 10% 1.00 25% 2.00 50% 2.00 75% 3.00 99% 5.00 max 20.00 Name: CNT_FAM_MEMBERS, dtype: float64
Out[23]:
<Axes: ylabel='CNT_FAM_MEMBERS'>
In [24]:
# Considering the count of family members, even though the 99th percentile is far from the max value 20 members is also possible
# Since we have only 2 rows with null values, we can fill it with 0
In [25]:
app_df['OCCUPATION_TYPE'].isna().sum()
Out[25]:
96391
In [26]:
# checking for maximum repeated value in OCCUPATION_TYPE
print(app_df['OCCUPATION_TYPE'].value_counts())
print('most repeated OCCUPATION_TYPE :',app_df['OCCUPATION_TYPE'].mode()[0])
OCCUPATION_TYPE Laborers 55186 Sales staff 32102 Core staff 27570 Managers 21371 Drivers 18603 High skill tech staff 11380 Accountants 9813 Medicine staff 8537 Security staff 6721 Cooking staff 5946 Cleaning staff 4653 Private service staff 2652 Low-skill Laborers 2093 Waiters/barmen staff 1348 Secretaries 1305 Realty agents 751 HR staff 563 IT staff 526 Name: count, dtype: int64 most repeated OCCUPATION_TYPE : Laborers
In [27]:
# Here the majority of the people who applied for the loan are laborers
# In this case there are 96391 null rows present. So the total number od null values are way more than the maximum employees.
# So to fill the null values with any occupation will cause a misleading report.
# We can fill the columns with 'Unknown'
# Use .loc for modifying the column
app_df.loc[:, 'OCCUPATION_TYPE'] = app_df['OCCUPATION_TYPE'].fillna(value='Unknown')
In [28]:
print(app_df['OCCUPATION_TYPE'].value_counts())
OCCUPATION_TYPE Unknown 96391 Laborers 55186 Sales staff 32102 Core staff 27570 Managers 21371 Drivers 18603 High skill tech staff 11380 Accountants 9813 Medicine staff 8537 Security staff 6721 Cooking staff 5946 Cleaning staff 4653 Private service staff 2652 Low-skill Laborers 2093 Waiters/barmen staff 1348 Secretaries 1305 Realty agents 751 HR staff 563 IT staff 526 Name: count, dtype: int64
In [29]:
# checking the null values after replacing 'Unknown' to occupation
app_df['OCCUPATION_TYPE'].isna().sum()
Out[29]:
0
In [30]:
sns.countplot(data = app_df, y = "OCCUPATION_TYPE", color = "turquoise")
Out[30]:
<Axes: xlabel='count', ylabel='OCCUPATION_TYPE'>
In [31]:
app_df['EXT_SOURCE_2'].isna().sum()
Out[31]:
660
In [32]:
print(round(app_df['EXT_SOURCE_2'].describe(percentiles=[0.1,0.25,0.5,0.75,0.99]),4))
sns.boxplot(app_df['EXT_SOURCE_2'])
count 306851.0000 mean 0.5144 std 0.1911 min 0.0000 10% 0.2157 25% 0.3925 50% 0.5660 75% 0.6636 99% 0.7828 max 0.8550 Name: EXT_SOURCE_2, dtype: float64
Out[32]:
<Axes: ylabel='EXT_SOURCE_2'>
In [33]:
# Here we cant see any outliers and there is no much difference between the 99th percentile and max value
# so that we can replace the null values by mean value of 0.5660
In [34]:
print(round(app_df['AMT_CREDIT'].describe(percentiles=[0.1,0.25,0.5,0.75,0.99]),2))
sns.boxplot(app_df['AMT_CREDIT'])
count 307511.00 mean 599026.00 std 402490.78 min 45000.00 10% 180000.00 25% 270000.00 50% 513531.00 75% 808650.00 99% 1854000.00 max 4050000.00 Name: AMT_CREDIT, dtype: float64
Out[34]:
<Axes: ylabel='AMT_CREDIT'>
In [35]:
# Here there are too much outliers and the max value is mothe than double the value of 99th percentile
# we can replace the null values with the mean
In [36]:
print(round(app_df['EXT_SOURCE_3'].describe(percentiles=[0.1,0.25,0.5,0.75,0.99]),4))
sns.boxplot(app_df['EXT_SOURCE_3'])
count 246546.0000 mean 0.5109 std 0.1948 min 0.0005 10% 0.2276 25% 0.3706 50% 0.5353 75% 0.6691 99% 0.8328 max 0.8960 Name: EXT_SOURCE_3, dtype: float64
Out[36]:
<Axes: ylabel='EXT_SOURCE_3'>
In [37]:
# since this column has more than 19% of null values even though there is no null values, we can drop the column
In [38]:
# 'AMT_REQ_CREDIT_BUREAU_YEAR',
# 'AMT_REQ_CREDIT_BUREAU_QRT',
# 'AMT_REQ_CREDIT_BUREAU_MON',
# 'AMT_REQ_CREDIT_BUREAU_WEEK',
# 'AMT_REQ_CREDIT_BUREAU_DAY',
# 'AMT_REQ_CREDIT_BUREAU_HOUR'
# All the above columns have same number of null values which is 41519
app_df[['AMT_REQ_CREDIT_BUREAU_YEAR',
'AMT_REQ_CREDIT_BUREAU_QRT',
'AMT_REQ_CREDIT_BUREAU_MON',
'AMT_REQ_CREDIT_BUREAU_WEEK',
'AMT_REQ_CREDIT_BUREAU_DAY',
'AMT_REQ_CREDIT_BUREAU_HOUR']].describe()
Out[38]:
| AMT_REQ_CREDIT_BUREAU_YEAR | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_HOUR | |
|---|---|---|---|---|---|---|
| count | 265992.000000 | 265992.000000 | 265992.000000 | 265992.000000 | 265992.000000 | 265992.000000 |
| mean | 1.899974 | 0.265474 | 0.267395 | 0.034362 | 0.007000 | 0.006402 |
| std | 1.869295 | 0.794056 | 0.916002 | 0.204685 | 0.110757 | 0.083849 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 3.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| max | 25.000000 | 261.000000 | 27.000000 | 8.000000 | 9.000000 | 4.000000 |
In [39]:
# As it has over 13% of null values we are nor replacing the data
In [40]:
print(round(app_df['OBS_30_CNT_SOCIAL_CIRCLE'].describe(percentiles=[0.1,0.25,0.5,0.75,0.99]),2))
sns.boxplot(app_df['OBS_30_CNT_SOCIAL_CIRCLE'])
count 306490.00 mean 1.42 std 2.40 min 0.00 10% 0.00 25% 0.00 50% 0.00 75% 2.00 99% 10.00 max 348.00 Name: OBS_30_CNT_SOCIAL_CIRCLE, dtype: float64
Out[40]:
<Axes: ylabel='OBS_30_CNT_SOCIAL_CIRCLE'>
In [41]:
# Since there are only 2 outliers and the null value percentage is also low we can replace it by median
In [42]:
# checking the unique elements of gender
app_df.CODE_GENDER.unique()
Out[42]:
array(['M', 'F', 'XNA'], dtype=object)
In [43]:
# checking the rows where gender is XNA
app_df[app_df['CODE_GENDER'] == 'XNA']
Out[43]:
| SK_ID_CURR | TARGET | NAME_CONTRACT_TYPE | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | NAME_TYPE_SUITE | NAME_INCOME_TYPE | NAME_EDUCATION_TYPE | NAME_FAMILY_STATUS | NAME_HOUSING_TYPE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | OCCUPATION_TYPE | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | WEEKDAY_APPR_PROCESS_START | HOUR_APPR_PROCESS_START | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | ORGANIZATION_TYPE | EXT_SOURCE_2 | EXT_SOURCE_3 | YEARS_BEGINEXPLUATATION_AVG | FLOORSMAX_AVG | YEARS_BEGINEXPLUATATION_MODE | FLOORSMAX_MODE | YEARS_BEGINEXPLUATATION_MEDI | FLOORSMAX_MEDI | TOTALAREA_MODE | EMERGENCYSTATE_MODE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35657 | 141289 | 0 | Revolving loans | XNA | Y | Y | 0 | 207000.0 | 382500.0 | 19125.0 | 337500.0 | Unaccompanied | Working | Secondary / secondary special | Married | Municipal apartment | 0.020713 | -20232 | -10044 | -10024.0 | -3537 | 1 | 1 | 1 | 1 | 1 | 0 | Unknown | 2.0 | 3 | 3 | TUESDAY | 10 | 0 | 0 | 0 | 0 | 0 | 0 | Business Entity Type 1 | 0.295998 | 0.461482 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | -286.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 38566 | 144669 | 0 | Revolving loans | XNA | N | Y | 2 | 157500.0 | 270000.0 | 13500.0 | 225000.0 | Family | Working | Secondary / secondary special | Married | House / apartment | 0.026392 | -13717 | -2797 | -2241.0 | -4659 | 1 | 1 | 1 | 1 | 1 | 0 | Low-skill Laborers | 4.0 | 2 | 2 | FRIDAY | 16 | 0 | 0 | 0 | 0 | 0 | 0 | Industry: type 3 | 0.709205 | 0.310818 | 0.9811 | 0.0417 | 0.9811 | 0.0417 | 0.9811 | 0.0417 | 0.0090 | No | 0.0 | 0.0 | 0.0 | 0.0 | -493.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 3.0 | 0.0 | 4.0 |
| 83382 | 196708 | 0 | Revolving loans | XNA | N | Y | 1 | 135000.0 | 405000.0 | 20250.0 | 225000.0 | Unaccompanied | Working | Higher education | Married | House / apartment | 0.035792 | -10647 | -1228 | -183.0 | -1671 | 1 | 1 | 1 | 1 | 1 | 0 | Core staff | 3.0 | 2 | 2 | THURSDAY | 15 | 0 | 0 | 0 | 0 | 0 | 0 | Kindergarten | 0.659185 | 0.076984 | 0.9921 | 0.1667 | 0.9921 | 0.1667 | 0.9921 | 0.1667 | 0.0769 | No | 7.0 | 1.0 | 7.0 | 1.0 | -851.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 189640 | 319880 | 0 | Revolving loans | XNA | Y | Y | 0 | 247500.0 | 540000.0 | 27000.0 | 900000.0 | Unaccompanied | Commercial associate | Incomplete higher | Civil marriage | House / apartment | 0.035792 | -9649 | -2293 | -4099.0 | -2326 | 1 | 1 | 1 | 1 | 1 | 0 | Unknown | 2.0 | 2 | 2 | FRIDAY | 15 | 0 | 0 | 0 | 0 | 0 | 0 | Medicine | 0.658620 | 0.360613 | 0.9712 | 0.0833 | 0.9712 | 0.0833 | 0.9712 | 0.0833 | 0.0245 | No | 10.0 | 4.0 | 10.0 | 4.0 | -1681.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 6.0 |
In [44]:
print(app_df['CODE_GENDER'].value_counts())
CODE_GENDER F 202448 M 105059 XNA 4 Name: count, dtype: int64
In [45]:
# since XNA is 4 and female count is comparitively low, we can replace it by 'F'
In [46]:
# we have evaluated with the null columns
# no we can find the correlation of the columns which affects target variable
In [47]:
print("Duplicate data: ", app_df.duplicated().sum())
# there is no duplicate rows present
Duplicate data: 0
In [48]:
print(app_df.corr(numeric_only=True)['TARGET'].sort_values(ascending=False))
TARGET 1.000000 DAYS_BIRTH 0.078239 REGION_RATING_CLIENT_W_CITY 0.060893 REGION_RATING_CLIENT 0.058899 DAYS_LAST_PHONE_CHANGE 0.055218 DAYS_ID_PUBLISH 0.051457 REG_CITY_NOT_WORK_CITY 0.050994 FLAG_EMP_PHONE 0.045982 REG_CITY_NOT_LIVE_CITY 0.044395 FLAG_DOCUMENT_3 0.044346 DAYS_REGISTRATION 0.041975 LIVE_CITY_NOT_WORK_CITY 0.032518 DEF_30_CNT_SOCIAL_CIRCLE 0.032248 DEF_60_CNT_SOCIAL_CIRCLE 0.031276 FLAG_WORK_PHONE 0.028524 AMT_REQ_CREDIT_BUREAU_YEAR 0.019930 CNT_CHILDREN 0.019187 CNT_FAM_MEMBERS 0.009308 OBS_30_CNT_SOCIAL_CIRCLE 0.009131 OBS_60_CNT_SOCIAL_CIRCLE 0.009022 REG_REGION_NOT_WORK_REGION 0.006942 REG_REGION_NOT_LIVE_REGION 0.005576 FLAG_DOCUMENT_2 0.005417 FLAG_DOCUMENT_21 0.003709 LIVE_REGION_NOT_WORK_REGION 0.002819 AMT_REQ_CREDIT_BUREAU_DAY 0.002704 AMT_REQ_CREDIT_BUREAU_HOUR 0.000930 AMT_REQ_CREDIT_BUREAU_WEEK 0.000788 FLAG_MOBIL 0.000534 FLAG_CONT_MOBILE 0.000370 FLAG_DOCUMENT_20 0.000215 FLAG_DOCUMENT_5 -0.000316 FLAG_DOCUMENT_12 -0.000756 FLAG_DOCUMENT_19 -0.001358 FLAG_DOCUMENT_10 -0.001414 FLAG_DOCUMENT_7 -0.001520 FLAG_EMAIL -0.001758 AMT_REQ_CREDIT_BUREAU_QRT -0.002022 SK_ID_CURR -0.002108 FLAG_DOCUMENT_4 -0.002672 FLAG_DOCUMENT_17 -0.003378 AMT_INCOME_TOTAL -0.003982 FLAG_DOCUMENT_11 -0.004229 FLAG_DOCUMENT_9 -0.004352 FLAG_DOCUMENT_15 -0.006536 FLAG_DOCUMENT_18 -0.007952 FLAG_DOCUMENT_8 -0.008040 YEARS_BEGINEXPLUATATION_MODE -0.009036 FLAG_DOCUMENT_14 -0.009464 YEARS_BEGINEXPLUATATION_AVG -0.009728 YEARS_BEGINEXPLUATATION_MEDI -0.009993 FLAG_DOCUMENT_13 -0.011583 FLAG_DOCUMENT_16 -0.011615 AMT_REQ_CREDIT_BUREAU_MON -0.012462 AMT_ANNUITY -0.012817 FLAG_PHONE -0.023806 HOUR_APPR_PROCESS_START -0.024166 FLAG_DOCUMENT_6 -0.028602 AMT_CREDIT -0.030369 TOTALAREA_MODE -0.032596 REGION_POPULATION_RELATIVE -0.037227 AMT_GOODS_PRICE -0.039645 FLOORSMAX_MODE -0.043226 FLOORSMAX_MEDI -0.043768 FLOORSMAX_AVG -0.044003 DAYS_EMPLOYED -0.044932 EXT_SOURCE_2 -0.160472 EXT_SOURCE_3 -0.178919 Name: TARGET, dtype: float64
In [49]:
app_df.loc[:, 'DAYS_BIRTH'] = app_df['DAYS_BIRTH'].abs()
app_df.loc[:, 'DAYS_EMPLOYED'] = app_df['DAYS_EMPLOYED'].abs()
app_df.loc[:, 'DAYS_ID_PUBLISH'] = app_df['DAYS_ID_PUBLISH'].abs()
app_df.loc[:, 'DAYS_LAST_PHONE_CHANGE'] = app_df['DAYS_LAST_PHONE_CHANGE'].abs()
# these columns cant be negative so replacing by absolute values
In [50]:
app_df.DAYS_BIRTH.describe()
Out[50]:
count 307511.000000 mean 16036.995067 std 4363.988632 min 7489.000000 25% 12413.000000 50% 15750.000000 75% 19682.000000 max 25229.000000 Name: DAYS_BIRTH, dtype: float64
In [258]:
app_df.loc[:, 'YEARS_BIRTH'] = app_df['DAYS_BIRTH'] // 365
print(app_df['YEARS_BIRTH'].describe())
sns.boxplot(data=app_df, x='YEARS_BIRTH')
count 307511.000000 mean 43.435968 std 11.954593 min 20.000000 25% 34.000000 50% 43.000000 75% 53.000000 max 69.000000 Name: YEARS_BIRTH, dtype: float64
Out[258]:
<Axes: xlabel='YEARS_BIRTH'>
In [52]:
print(app_df.corr(numeric_only=True)['TARGET'].sort_values(ascending=False))
TARGET 1.000000 REGION_RATING_CLIENT_W_CITY 0.060893 REGION_RATING_CLIENT 0.058899 REG_CITY_NOT_WORK_CITY 0.050994 FLAG_EMP_PHONE 0.045982 REG_CITY_NOT_LIVE_CITY 0.044395 FLAG_DOCUMENT_3 0.044346 DAYS_REGISTRATION 0.041975 LIVE_CITY_NOT_WORK_CITY 0.032518 DEF_30_CNT_SOCIAL_CIRCLE 0.032248 DEF_60_CNT_SOCIAL_CIRCLE 0.031276 FLAG_WORK_PHONE 0.028524 AMT_REQ_CREDIT_BUREAU_YEAR 0.019930 CNT_CHILDREN 0.019187 CNT_FAM_MEMBERS 0.009308 OBS_30_CNT_SOCIAL_CIRCLE 0.009131 OBS_60_CNT_SOCIAL_CIRCLE 0.009022 REG_REGION_NOT_WORK_REGION 0.006942 REG_REGION_NOT_LIVE_REGION 0.005576 FLAG_DOCUMENT_2 0.005417 FLAG_DOCUMENT_21 0.003709 LIVE_REGION_NOT_WORK_REGION 0.002819 AMT_REQ_CREDIT_BUREAU_DAY 0.002704 AMT_REQ_CREDIT_BUREAU_HOUR 0.000930 AMT_REQ_CREDIT_BUREAU_WEEK 0.000788 FLAG_MOBIL 0.000534 FLAG_CONT_MOBILE 0.000370 FLAG_DOCUMENT_20 0.000215 FLAG_DOCUMENT_5 -0.000316 FLAG_DOCUMENT_12 -0.000756 FLAG_DOCUMENT_19 -0.001358 FLAG_DOCUMENT_10 -0.001414 FLAG_DOCUMENT_7 -0.001520 FLAG_EMAIL -0.001758 AMT_REQ_CREDIT_BUREAU_QRT -0.002022 SK_ID_CURR -0.002108 FLAG_DOCUMENT_4 -0.002672 FLAG_DOCUMENT_17 -0.003378 AMT_INCOME_TOTAL -0.003982 FLAG_DOCUMENT_11 -0.004229 FLAG_DOCUMENT_9 -0.004352 FLAG_DOCUMENT_15 -0.006536 FLAG_DOCUMENT_18 -0.007952 FLAG_DOCUMENT_8 -0.008040 YEARS_BEGINEXPLUATATION_MODE -0.009036 FLAG_DOCUMENT_14 -0.009464 YEARS_BEGINEXPLUATATION_AVG -0.009728 YEARS_BEGINEXPLUATATION_MEDI -0.009993 FLAG_DOCUMENT_13 -0.011583 FLAG_DOCUMENT_16 -0.011615 AMT_REQ_CREDIT_BUREAU_MON -0.012462 AMT_ANNUITY -0.012817 FLAG_PHONE -0.023806 HOUR_APPR_PROCESS_START -0.024166 FLAG_DOCUMENT_6 -0.028602 AMT_CREDIT -0.030369 TOTALAREA_MODE -0.032596 REGION_POPULATION_RELATIVE -0.037227 AMT_GOODS_PRICE -0.039645 FLOORSMAX_MODE -0.043226 FLOORSMAX_MEDI -0.043768 FLOORSMAX_AVG -0.044003 DAYS_EMPLOYED -0.047046 DAYS_ID_PUBLISH -0.051457 DAYS_LAST_PHONE_CHANGE -0.055218 YEARS_BIRTH -0.078234 DAYS_BIRTH -0.078239 EXT_SOURCE_2 -0.160472 EXT_SOURCE_3 -0.178919 Name: TARGET, dtype: float64
In [53]:
# checking the NAME_FAMILY_STATUS of applicants
app_df['NAME_FAMILY_STATUS'].value_counts()
Out[53]:
NAME_FAMILY_STATUS Married 196432 Single / not married 45444 Civil marriage 29775 Separated 19770 Widow 16088 Unknown 2 Name: count, dtype: int64
In [54]:
# since 2 person's family status is 'unknown' we cant just allocate them to any category so we fill it with single
In [256]:
app_df.loc[:, 'YEARS_EMPLOYED'] = app_df['DAYS_EMPLOYED'] // 365
print(app_df['YEARS_EMPLOYED'].describe())
sns.boxplot(data=app_df, x='YEARS_EMPLOYED')
count 307511.000000 mean 185.021521 std 381.972190 min 0.000000 25% 2.000000 50% 6.000000 75% 15.000000 max 1000.000000 Name: YEARS_EMPLOYED, dtype: float64
Out[256]:
<Axes: xlabel='YEARS_EMPLOYED'>
In [56]:
app_df.YEARS_EMPLOYED.value_counts()
Out[56]:
YEARS_EMPLOYED 1000 55374 1 31841 2 29648 0 27904 3 25107 4 21767 5 16271 6 15051 7 13148 8 11440 9 8980 10 7364 11 5902 12 5309 14 4643 13 4349 15 2725 16 2335 17 2006 18 1918 19 1871 20 1600 21 1461 22 1253 23 1016 24 914 25 821 27 656 26 653 28 611 29 567 30 457 31 437 34 364 32 351 33 331 35 257 36 196 37 138 39 125 38 116 40 59 41 59 42 42 44 31 43 19 45 14 48 4 46 4 47 1 49 1 Name: count, dtype: int64
In [57]:
# 55374 persons in this data shows 1000 as total no of employment which is a wrong entry and also an outlier
In [254]:
# creating a new column YEARS_ID_PUBLISH for ease of analysis
app_df.loc[:,'YEARS_ID_PUBLISH'] = app_df['DAYS_ID_PUBLISH'] // 365
print(app_df['YEARS_ID_PUBLISH'].describe())
sns.boxplot(data=app_df, x='YEARS_ID_PUBLISH')
count 307511.000000 mean 7.713474 std 4.134515 min 0.000000 25% 4.000000 50% 8.000000 75% 11.000000 max 19.000000 Name: YEARS_ID_PUBLISH, dtype: float64
Out[254]:
<Axes: xlabel='YEARS_ID_PUBLISH'>
In [59]:
# there are no outliersArithmeticError
In [252]:
# creating a new column YEARS_LAST_PHONE_CHANGE for ease of analysis
app_df.loc[:,'YEARS_LAST_PHONE_CHANGE'] = app_df['DAYS_LAST_PHONE_CHANGE'] // 365
print(app_df['YEARS_LAST_PHONE_CHANGE'].describe())
sns.boxplot(data=app_df, x='YEARS_LAST_PHONE_CHANGE')
count 307510.000000 mean 2.225115 std 2.193678 min 0.000000 25% 0.000000 50% 2.000000 75% 4.000000 max 11.000000 Name: YEARS_LAST_PHONE_CHANGE, dtype: float64
Out[252]:
<Axes: xlabel='YEARS_LAST_PHONE_CHANGE'>
In [61]:
app_df.YEARS_LAST_PHONE_CHANGE.value_counts()
Out[61]:
YEARS_LAST_PHONE_CHANGE 0.0 92451 1.0 57641 4.0 38061 2.0 37939 3.0 29929 5.0 22131 6.0 15203 7.0 8316 8.0 4193 9.0 1423 10.0 194 11.0 29 Name: count, dtype: int64
In [62]:
# there are 29 outliers having value 11
# most of the applicants who have changed their phones fall between 0-4 years
In [63]:
# checking the count of target variable
plt.figure(figsize = [7,3])
sns.countplot(data=app_df, x='TARGET')
plt.title("Checking imbalance ratio of TARGET variable")
plt.xlabel("\n 0 - On-time Payment clients : 1 - Clients with Payment Difficulty")
Out[63]:
Text(0.5, 0, '\n 0 - On-time Payment clients : 1 - Clients with Payment Difficulty')
In [64]:
app_df.TARGET.value_counts()
Out[64]:
TARGET 0 282686 1 24825 Name: count, dtype: int64
In [65]:
# target column has 2 values
# 1 indicates the clients with payment difficulty, late payment or cancelled payment
# 0 indicates on time payment clients
In [66]:
# the ratio between on-time payment clients vs clients with payment difficulty
282686/24825
Out[66]:
11.387150050352467
In [67]:
# to check in percentage
print(app_df['TARGET'].value_counts(normalize=True)*100)
TARGET 0 91.927118 1 8.072882 Name: proportion, dtype: float64
In [68]:
# 91.92% clients are on time payers
# 8.07% clients have difficulty in paying loan
In [69]:
# creating new dataframe with TARGET value
df0 = app_df[app_df['TARGET'] == 0]
df1 = app_df[app_df['TARGET'] == 1]
In [70]:
# analysis on the categorical column
# checking all columns with object type data(string) and storing it in a list
obj_plot=list(app_df.columns[app_df.dtypes=="object"])
obj_plot
Out[70]:
['NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'NAME_TYPE_SUITE', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'OCCUPATION_TYPE', 'WEEKDAY_APPR_PROCESS_START', 'ORGANIZATION_TYPE', 'EMERGENCYSTATE_MODE']
In [71]:
# checking each columns underlying groups
for i in obj_plot:
# for clients with payment difficulties
print("--------------------------------------------------")
print(f"{i} for clients with payment difficulties")
print(df1[i].value_counts().sort_values(ascending = False))
# for on-time payment clients
print(f"\n{i} for on-time payment clients")
print(df0[i].value_counts().sort_values(ascending = False))
-------------------------------------------------- NAME_CONTRACT_TYPE for clients with payment difficulties NAME_CONTRACT_TYPE Cash loans 23221 Revolving loans 1604 Name: count, dtype: int64 NAME_CONTRACT_TYPE for on-time payment clients NAME_CONTRACT_TYPE Cash loans 255011 Revolving loans 27675 Name: count, dtype: int64 -------------------------------------------------- CODE_GENDER for clients with payment difficulties CODE_GENDER F 14170 M 10655 Name: count, dtype: int64 CODE_GENDER for on-time payment clients CODE_GENDER F 188278 M 94404 XNA 4 Name: count, dtype: int64 -------------------------------------------------- FLAG_OWN_CAR for clients with payment difficulties FLAG_OWN_CAR N 17249 Y 7576 Name: count, dtype: int64 FLAG_OWN_CAR for on-time payment clients FLAG_OWN_CAR N 185675 Y 97011 Name: count, dtype: int64 -------------------------------------------------- FLAG_OWN_REALTY for clients with payment difficulties FLAG_OWN_REALTY Y 16983 N 7842 Name: count, dtype: int64 FLAG_OWN_REALTY for on-time payment clients FLAG_OWN_REALTY Y 196329 N 86357 Name: count, dtype: int64 -------------------------------------------------- NAME_TYPE_SUITE for clients with payment difficulties NAME_TYPE_SUITE Unaccompanied 20337 Family 3009 Spouse, partner 895 Children 241 Other_B 174 Other_A 76 Group of people 23 Name: count, dtype: int64 NAME_TYPE_SUITE for on-time payment clients NAME_TYPE_SUITE Unaccompanied 228189 Family 37140 Spouse, partner 10475 Children 3026 Other_B 1596 Other_A 790 Group of people 248 Name: count, dtype: int64 -------------------------------------------------- NAME_INCOME_TYPE for clients with payment difficulties NAME_INCOME_TYPE Working 15224 Commercial associate 5360 Pensioner 2982 State servant 1249 Unemployed 8 Maternity leave 2 Name: count, dtype: int64 NAME_INCOME_TYPE for on-time payment clients NAME_INCOME_TYPE Working 143550 Commercial associate 66257 Pensioner 52380 State servant 20454 Student 18 Unemployed 14 Businessman 10 Maternity leave 3 Name: count, dtype: int64 -------------------------------------------------- NAME_EDUCATION_TYPE for clients with payment difficulties NAME_EDUCATION_TYPE Secondary / secondary special 19524 Higher education 4009 Incomplete higher 872 Lower secondary 417 Academic degree 3 Name: count, dtype: int64 NAME_EDUCATION_TYPE for on-time payment clients NAME_EDUCATION_TYPE Secondary / secondary special 198867 Higher education 70854 Incomplete higher 9405 Lower secondary 3399 Academic degree 161 Name: count, dtype: int64 -------------------------------------------------- NAME_FAMILY_STATUS for clients with payment difficulties NAME_FAMILY_STATUS Married 14850 Single / not married 4457 Civil marriage 2961 Separated 1620 Widow 937 Name: count, dtype: int64 NAME_FAMILY_STATUS for on-time payment clients NAME_FAMILY_STATUS Married 181582 Single / not married 40987 Civil marriage 26814 Separated 18150 Widow 15151 Unknown 2 Name: count, dtype: int64 -------------------------------------------------- NAME_HOUSING_TYPE for clients with payment difficulties NAME_HOUSING_TYPE House / apartment 21272 With parents 1736 Municipal apartment 955 Rented apartment 601 Office apartment 172 Co-op apartment 89 Name: count, dtype: int64 NAME_HOUSING_TYPE for on-time payment clients NAME_HOUSING_TYPE House / apartment 251596 With parents 13104 Municipal apartment 10228 Rented apartment 4280 Office apartment 2445 Co-op apartment 1033 Name: count, dtype: int64 -------------------------------------------------- OCCUPATION_TYPE for clients with payment difficulties OCCUPATION_TYPE Unknown 6278 Laborers 5838 Sales staff 3092 Drivers 2107 Core staff 1738 Managers 1328 Security staff 722 High skill tech staff 701 Cooking staff 621 Medicine staff 572 Accountants 474 Cleaning staff 447 Low-skill Laborers 359 Private service staff 175 Waiters/barmen staff 152 Secretaries 92 Realty agents 59 HR staff 36 IT staff 34 Name: count, dtype: int64 OCCUPATION_TYPE for on-time payment clients OCCUPATION_TYPE Unknown 90113 Laborers 49348 Sales staff 29010 Core staff 25832 Managers 20043 Drivers 16496 High skill tech staff 10679 Accountants 9339 Medicine staff 7965 Security staff 5999 Cooking staff 5325 Cleaning staff 4206 Private service staff 2477 Low-skill Laborers 1734 Secretaries 1213 Waiters/barmen staff 1196 Realty agents 692 HR staff 527 IT staff 492 Name: count, dtype: int64 -------------------------------------------------- WEEKDAY_APPR_PROCESS_START for clients with payment difficulties WEEKDAY_APPR_PROCESS_START TUESDAY 4501 WEDNESDAY 4238 FRIDAY 4101 THURSDAY 4098 MONDAY 3934 SATURDAY 2670 SUNDAY 1283 Name: count, dtype: int64 WEEKDAY_APPR_PROCESS_START for on-time payment clients WEEKDAY_APPR_PROCESS_START TUESDAY 49400 WEDNESDAY 47696 MONDAY 46780 THURSDAY 46493 FRIDAY 46237 SATURDAY 31182 SUNDAY 14898 Name: count, dtype: int64 -------------------------------------------------- ORGANIZATION_TYPE for clients with payment difficulties ORGANIZATION_TYPE Business Entity Type 3 6323 Self-employed 3908 XNA 2990 Other 1275 Business Entity Type 2 900 Construction 785 Trade: type 7 740 Medicine 737 Government 726 School 526 Transport: type 4 501 Business Entity Type 1 487 Kindergarten 484 Trade: type 3 361 Industry: type 3 348 Security 324 Agriculture 257 Housing 235 Industry: type 11 234 Industry: type 9 225 Restaurant 212 Transport: type 3 187 Postal 182 Transport: type 2 172 Military 135 Trade: type 2 133 Bank 130 Police 117 Industry: type 1 115 Industry: type 7 105 Services 104 Security Ministries 96 Industry: type 4 89 University 65 Electricity 63 Hotel 62 Telecom 44 Realtor 42 Industry: type 5 41 Emergency 40 Advertising 35 Insurance 34 Industry: type 2 33 Trade: type 1 31 Cleaning 29 Mobile 29 Trade: type 6 29 Legal Services 24 Culture 21 Industry: type 12 14 Industry: type 13 9 Transport: type 1 9 Industry: type 6 8 Industry: type 10 7 Religion 5 Trade: type 5 3 Industry: type 8 3 Trade: type 4 2 Name: count, dtype: int64 ORGANIZATION_TYPE for on-time payment clients ORGANIZATION_TYPE Business Entity Type 3 61669 XNA 52384 Self-employed 34504 Other 15408 Medicine 10456 Government 9678 Business Entity Type 2 9653 School 8367 Trade: type 7 7091 Kindergarten 6396 Construction 5936 Business Entity Type 1 5497 Transport: type 4 4897 Industry: type 9 3143 Trade: type 3 3131 Industry: type 3 2930 Security 2923 Housing 2723 Military 2499 Industry: type 11 2470 Bank 2377 Police 2224 Agriculture 2197 Transport: type 2 2032 Postal 1975 Security Ministries 1878 Trade: type 2 1767 Restaurant 1599 Services 1471 University 1262 Industry: type 7 1202 Transport: type 3 1000 Industry: type 1 924 Hotel 904 Electricity 887 Industry: type 4 788 Trade: type 6 602 Insurance 563 Industry: type 5 558 Telecom 533 Emergency 520 Industry: type 2 425 Advertising 394 Culture 358 Industry: type 12 355 Realtor 354 Trade: type 1 317 Mobile 288 Legal Services 281 Cleaning 231 Transport: type 1 192 Industry: type 6 104 Industry: type 10 102 Religion 80 Trade: type 4 62 Industry: type 13 58 Trade: type 5 46 Industry: type 8 21 Name: count, dtype: int64 -------------------------------------------------- EMERGENCYSTATE_MODE for clients with payment difficulties EMERGENCYSTATE_MODE No 11104 Yes 223 Name: count, dtype: int64 EMERGENCYSTATE_MODE for on-time payment clients EMERGENCYSTATE_MODE No 148324 Yes 2105 Name: count, dtype: int64
In [72]:
# Plotting a count plot on TARGET's object columns
c_plot=['NAME_CONTRACT_TYPE','CODE_GENDER','FLAG_OWN_CAR','FLAG_OWN_REALTY','NAME_INCOME_TYPE','NAME_EDUCATION_TYPE','NAME_FAMILY_STATUS','NAME_HOUSING_TYPE','OCCUPATION_TYPE','WEEKDAY_APPR_PROCESS_START','ORGANIZATION_TYPE']
for i in c_plot:
plt.figure(figsize = [18,8])
# for clients with payment difficulties
plt.subplot(1,2,1)
plt.title(f'{i} of clients with payment difficulties')
sns.countplot(data=df1, x =i, order = sorted(df1[i].unique(), reverse = True))
plt.xticks(rotation = 90)
# for on-time payment clients
plt.subplot(1,2,2)
plt.title(f'{i} of clients with on-time payments')
sns.countplot(data=df0, x =i, order = sorted(df1[i].unique(), reverse = True))
plt.xticks(rotation = 90)
plt.tight_layout(pad = 4)
In [73]:
# Plotting a bar chart on TARGET's object columns which are categorical
b_plot=['NAME_INCOME_TYPE','NAME_EDUCATION_TYPE','NAME_HOUSING_TYPE','OCCUPATION_TYPE','WEEKDAY_APPR_PROCESS_START','ORGANIZATION_TYPE']
for i in b_plot:
plt.figure(figsize = [20,8])
# for clients with payment difficulties
plt.subplot(1,2,1)
(df1[i].value_counts(normalize=True)*100).plot.bar(title = i + " of clients with payment difficulties in %", color=['indianred', 'dodgerblue', 'darkcyan', 'lightslategrey', 'lightseagreen'])
plt.xticks(rotation=90)
# for on-time payment clients
plt.subplot(1,2,2)
(df0[i].value_counts(normalize=True)*100).plot.bar(title = i + " of clients with on-Time Payments in %", color=['indianred', 'dodgerblue', 'darkcyan', 'lightslategrey', 'lightseagreen' ])
plt.xticks(rotation=90)
plt.tight_layout(pad = 4)
In [74]:
p_plot=['NAME_CONTRACT_TYPE','CODE_GENDER','FLAG_OWN_CAR','FLAG_OWN_REALTY','NAME_FAMILY_STATUS','WEEKDAY_APPR_PROCESS_START']
# Plotting a pie chart on TARGET's object columns
for i in p_plot:
plt.figure(figsize = [20,12])
# for clients with payment difficulties
plt.subplot(1,2,1)
plt.title(f'{i} of clients with payment difficulties')
df1[i].value_counts().plot.pie(autopct='%1.1f%%',shadow=True, startangle=60, labeldistance=None)
plt.legend()
# for on-time payment clients
plt.subplot(1,2,2)
plt.title(f'{i} of clients with on-time payments')
df0[i].value_counts().plot.pie(autopct='%1.1f%%',shadow=True, startangle=60, labeldistance=None)
plt.legend()
plt.tight_layout(pad = 4)
In [75]:
# analysis on numeric column
# checking out total number of numeric columns
app_df.columns[(app_df.dtypes=="int64") | (app_df.dtypes=="float64")]
Out[75]:
Index(['SK_ID_CURR', 'TARGET', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL',
'AMT_CREDIT', 'AMT_ANNUITY', 'AMT_GOODS_PRICE',
'REGION_POPULATION_RELATIVE', 'DAYS_BIRTH', 'DAYS_EMPLOYED',
'DAYS_REGISTRATION', 'DAYS_ID_PUBLISH', 'FLAG_MOBIL', 'FLAG_EMP_PHONE',
'FLAG_WORK_PHONE', 'FLAG_CONT_MOBILE', 'FLAG_PHONE', 'FLAG_EMAIL',
'CNT_FAM_MEMBERS', 'REGION_RATING_CLIENT',
'REGION_RATING_CLIENT_W_CITY', 'HOUR_APPR_PROCESS_START',
'REG_REGION_NOT_LIVE_REGION', 'REG_REGION_NOT_WORK_REGION',
'LIVE_REGION_NOT_WORK_REGION', 'REG_CITY_NOT_LIVE_CITY',
'REG_CITY_NOT_WORK_CITY', 'LIVE_CITY_NOT_WORK_CITY', 'EXT_SOURCE_2',
'EXT_SOURCE_3', 'YEARS_BEGINEXPLUATATION_AVG', 'FLOORSMAX_AVG',
'YEARS_BEGINEXPLUATATION_MODE', 'FLOORSMAX_MODE',
'YEARS_BEGINEXPLUATATION_MEDI', 'FLOORSMAX_MEDI', 'TOTALAREA_MODE',
'OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE',
'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE',
'DAYS_LAST_PHONE_CHANGE', 'FLAG_DOCUMENT_2', 'FLAG_DOCUMENT_3',
'FLAG_DOCUMENT_4', 'FLAG_DOCUMENT_5', 'FLAG_DOCUMENT_6',
'FLAG_DOCUMENT_7', 'FLAG_DOCUMENT_8', 'FLAG_DOCUMENT_9',
'FLAG_DOCUMENT_10', 'FLAG_DOCUMENT_11', 'FLAG_DOCUMENT_12',
'FLAG_DOCUMENT_13', 'FLAG_DOCUMENT_14', 'FLAG_DOCUMENT_15',
'FLAG_DOCUMENT_16', 'FLAG_DOCUMENT_17', 'FLAG_DOCUMENT_18',
'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20', 'FLAG_DOCUMENT_21',
'AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY',
'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON',
'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR',
'YEARS_BIRTH', 'YEARS_EMPLOYED', 'YEARS_ID_PUBLISH',
'YEARS_LAST_PHONE_CHANGE'],
dtype='object')
In [76]:
# Drop all columns starting with "FLAG"
app_df = app_df.drop(columns=[col for col in app_df.columns if col.startswith("FLAG")])
In [77]:
app_df.columns[(app_df.dtypes=="int64") | (app_df.dtypes=="float64")]
Out[77]:
Index(['SK_ID_CURR', 'TARGET', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL',
'AMT_CREDIT', 'AMT_ANNUITY', 'AMT_GOODS_PRICE',
'REGION_POPULATION_RELATIVE', 'DAYS_BIRTH', 'DAYS_EMPLOYED',
'DAYS_REGISTRATION', 'DAYS_ID_PUBLISH', 'CNT_FAM_MEMBERS',
'REGION_RATING_CLIENT', 'REGION_RATING_CLIENT_W_CITY',
'HOUR_APPR_PROCESS_START', 'REG_REGION_NOT_LIVE_REGION',
'REG_REGION_NOT_WORK_REGION', 'LIVE_REGION_NOT_WORK_REGION',
'REG_CITY_NOT_LIVE_CITY', 'REG_CITY_NOT_WORK_CITY',
'LIVE_CITY_NOT_WORK_CITY', 'EXT_SOURCE_2', 'EXT_SOURCE_3',
'YEARS_BEGINEXPLUATATION_AVG', 'FLOORSMAX_AVG',
'YEARS_BEGINEXPLUATATION_MODE', 'FLOORSMAX_MODE',
'YEARS_BEGINEXPLUATATION_MEDI', 'FLOORSMAX_MEDI', 'TOTALAREA_MODE',
'OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE',
'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE',
'DAYS_LAST_PHONE_CHANGE', 'AMT_REQ_CREDIT_BUREAU_HOUR',
'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK',
'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT',
'AMT_REQ_CREDIT_BUREAU_YEAR', 'YEARS_BIRTH', 'YEARS_EMPLOYED',
'YEARS_ID_PUBLISH', 'YEARS_LAST_PHONE_CHANGE'],
dtype='object')
In [174]:
d_plot = ['AMT_CREDIT', 'YEARS_BIRTH', 'AMT_GOODS_PRICE', 'DAYS_EMPLOYED', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL']
for i in d_plot:
# Calculate IQR for clients with payment difficulties
df1_Q1 = df1[i].quantile(0.25)
df1_Q3 = df1[i].quantile(0.75)
df1_IQR = df1_Q3 - df1_Q1
Min_value1 = (df1_Q1 - 1.5 * df1_IQR)
Max_value1 = (df1_Q3 + 1.5 * df1_IQR)
# Calculate IQR for clients with on-time payments
df0_Q1 = df0[i].quantile(0.25)
df0_Q3 = df0[i].quantile(0.75)
df0_IQR = df0_Q3 - df0_Q1
Min_value0 = (df0_Q1 - 1.5 * df0_IQR)
Max_value0 = (df0_Q3 + 1.5 * df0_IQR)
# Remove outliers and plot using displot
plt.figure(figsize=[20,8])
# Plot for clients with payment difficulties
sns.displot(df1[df1[i] <= Max_value1], x=i, kde=True, label='Payment difficulties', color='blue')
# Plot for clients with on-time payments
sns.displot(df0[df0[i] <= Max_value0], x=i, kde=True, label='On-Time Payments', color='green')
plt.title(f'{i} of clients')
plt.ticklabel_format(style='plain', axis='x')
plt.legend()
plt.show()
<Figure size 2000x800 with 0 Axes>
<Figure size 2000x800 with 0 Axes>
<Figure size 2000x800 with 0 Axes>
<Figure size 2000x800 with 0 Axes>
<Figure size 2000x800 with 0 Axes>
<Figure size 2000x800 with 0 Axes>
In [ ]:
Observations:
Fοr AMT_CREDIT between 250000 and apprοximately 650000, there are mοre clients with Payment difficulties
Fοr AMT_CREDIT > 750000 , there are mοre clients with οn-Time Payments
Fοr YEARS_BIRTH between 20 and 40, there are mοre clients with Payment difficulties
Fοr YEARS_BIRTH > 40 , there are mοre clients with οn-Time Payments
Fοr AMT_GOODS_PRICE between ~250000 and ~550000, there are mοre clients with Payment difficulties
For DAYS_EMPLOYED less than 2000, there are mοre clients with Payment difficulties
Fοr DAYS_EMPLOYED > 2000 , there are mοre clients with οn-Time Payments, impluing that thοse whο are emplοyed lοnger have better chances οf repaying the lοan
Fοr CNT_CHILDREN=0 (thοse with nο children), there are lοts οf clients with οn-Time Payments
Fοr CNT_CHILDREN with 1 οR 2 (thοse with 1 οr 2 children), there are few mοre clients with οn-Time Payments
Fοr clients with Payment difficulties, the AMT_INCOME_TOTAL distributiοn resembles a nοrmal distributiοn apprοximately
In [182]:
# making bins for YEARS_BIRTH and creating new column "AGE_GROUP"
app_df['AGE_GROUP']= pd.cut(app_df['YEARS_BIRTH'],bins=[15,25,35,45,55,65,75])
(app_df['AGE_GROUP'].value_counts(normalize=True)*100).plot.barh(title ="Age group of clients applying for loan", color=['indianred', 'dodgerblue', 'darkcyan', 'lightslategrey', 'lightseagreen' ])
Out[182]:
<Axes: title={'center': 'Age group of clients applying for loan'}, ylabel='AGE_GROUP'>
In [ ]:
# 35-45 is the largest age group who apply for loans
In [190]:
# making bins for 'AMT_INCOME_TOTAL' and Creating new column "INCOME_GROUP"
app_df['INCOME_GROUP']= pd.qcut(app_df['AMT_INCOME_TOTAL'], q=[0,0.1,0.3,0.6,0.8,1],labels=['VeryLow','Low','Medium','High','VeryHigh'])
(app_df['INCOME_GROUP'].value_counts(normalize=True)*100).plot.barh(title ="Salary group of clients applying for loan", color=['indianred', 'dodgerblue', 'darkcyan', 'lightslategrey', 'lightseagreen' ])
Out[190]:
<Axes: title={'center': 'Salary group of clients applying for loan'}, ylabel='INCOME_GROUP'>
In [ ]:
# 'Medium' Incοme grοup is the largest grοup applying fοr lοans, fοllοwed by 'High' incοme grοup. 'VeryLοw' incοme grοup is the smallest grοup applying fοr loan.
In [192]:
# function to calculate min max value for IQR
def outlier_range(dataset,column):
Q1 = dataset[column].quantile(0.25)
Q3 = dataset[column].quantile(0.75)
IQR = Q3 - Q1
Min_value = (Q1 - 1.5 * IQR)
Max_value = (Q3 + 1.5 * IQR)
return Max_value
In [194]:
# outlier analysis of AMT_GOODS_PRICE V/S AMT_CREDIT
max_value1_AMT_GOODS_PRICE = outlier_range(df1,'AMT_GOODS_PRICE')
max_value1_AMT_CREDIT = outlier_range(df1,'AMT_CREDIT')
max_value0_AMT_GOODS_PRICE = outlier_range(df0,'AMT_GOODS_PRICE')
max_value0_AMT_CREDIT = outlier_range(df0,'AMT_CREDIT')
In [196]:
# plotting a scatter plot to see the relation
plt.figure(figsize = [20,8])
plt.subplot(1,2,1)
plt.title('Payment difficulties')
sns.scatterplot(x = df1[df1['AMT_GOODS_PRICE'] < max_value1_AMT_GOODS_PRICE].AMT_GOODS_PRICE, y = df1[df1['AMT_CREDIT'] < max_value1_AMT_CREDIT].AMT_CREDIT, data = df1)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')
plt.subplot(1,2,2)
plt.title('On-Time Payments')
sns.scatterplot(x = df0[df0['AMT_GOODS_PRICE'] < max_value0_AMT_GOODS_PRICE].AMT_GOODS_PRICE, y = df0[df0['AMT_CREDIT'] < max_value0_AMT_CREDIT].AMT_CREDIT, data = df0)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')
plt.tight_layout(pad = 4)
In [ ]:
# AMT_GOODS_PRICE and AMT_CREDIT have strοng pοsitive cοrrelatiοn. This means that as Gοοds price increases, sο dοes Credit Amοunt
In [198]:
# outlier analysis of AMT_ANNUITY V/S AMT_CREDIT
max_value1_AMT_ANNUITY = outlier_range(df1,'AMT_ANNUITY')
max_value1_AMT_CREDIT = outlier_range(df1,'AMT_CREDIT')
max_value0_AMT_ANNUITY = outlier_range(df0,'AMT_ANNUITY')
max_value0_AMT_CREDIT = outlier_range(df0,'AMT_CREDIT')
In [200]:
# plotting a scatter plot to see the relation
plt.figure(figsize = [20,8])
plt.subplot(1,2,1)
plt.title('Payment difficulties')
sns.scatterplot(x = df1[df1['AMT_ANNUITY'] < max_value1_AMT_ANNUITY].AMT_ANNUITY, y = df1[df1['AMT_CREDIT'] < max_value1_AMT_CREDIT].AMT_CREDIT, data = df1)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')
plt.subplot(1,2,2)
plt.title('On-Time Payments')
sns.scatterplot(x = df0[df0['AMT_ANNUITY'] < max_value0_AMT_ANNUITY].AMT_ANNUITY, y = df0[df0['AMT_CREDIT'] < max_value0_AMT_CREDIT].AMT_CREDIT, data = df0)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')
plt.tight_layout(pad = 4)
In [ ]:
# AMT_ANNUITY and AMT_CREDIT have strοng pοsitive cοrrelatiοn. This means that as Annuity Amοunt increases, sο dοes Credit Amount
In [202]:
# outlier analysis of DAYS_EMPLOYED V/S AMT_INCOME_TOTAL
max_value1_DAYS_EMPLOYED = outlier_range(df1,'DAYS_EMPLOYED')
max_value1_AMT_INCOME_TOTAL = outlier_range(df1,'AMT_INCOME_TOTAL')
max_value0_DAYS_EMPLOYED = outlier_range(df0,'DAYS_EMPLOYED')
max_value0_AMT_INCOME_TOTAL = outlier_range(df0,'AMT_INCOME_TOTAL')
In [204]:
# plotting a scatter plot to see the relation
plt.figure(figsize = [20,8])
plt.subplot(1,2,1)
plt.title('Payment difficulties')
sns.scatterplot(x = df1[df1['DAYS_EMPLOYED'] < max_value1_DAYS_EMPLOYED].DAYS_EMPLOYED, y = df1[df1['AMT_INCOME_TOTAL'] < max_value1_AMT_INCOME_TOTAL].AMT_INCOME_TOTAL, data = df1)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')
plt.subplot(1,2,2)
plt.title('On-Time Payments')
sns.scatterplot(x = df0[df0['DAYS_EMPLOYED'] < max_value0_DAYS_EMPLOYED].DAYS_EMPLOYED, y = df0[df0['AMT_INCOME_TOTAL'] < max_value0_AMT_INCOME_TOTAL].AMT_INCOME_TOTAL, data = df0)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')
plt.tight_layout(pad = 4)
In [ ]:
# Clients whο are emplοyed fοr a lοng time (>7000) days are making their payments οn-time but these categοry οf clients dο nοt exist in Payments difficulties grοup
# Even lοοking at Payment difficulties grοup, clients with mοre than 4000 days οf employment are sparse
In [206]:
# outlier analysis of AMT_ANNUITY V/S AMT_GOODS_PRICE
max_value1_AMT_CREDIT = outlier_range(df1,'AMT_ANNUITY')
max_value1_DAYS_BIRTH = outlier_range(df1,'AMT_GOODS_PRICE')
max_value0_AMT_CREDIT = outlier_range(df0,'AMT_ANNUITY')
max_value0_DAYS_BIRTH = outlier_range(df0,'AMT_GOODS_PRICE')
In [208]:
# plotting a scatter plot to see the relation
plt.figure(figsize = [20,8])
plt.subplot(1,2,1)
plt.title('Payment difficulties')
sns.scatterplot(x = df1[df1['AMT_ANNUITY'] < max_value1_AMT_ANNUITY].AMT_ANNUITY, y = df1[df1['AMT_GOODS_PRICE'] < max_value1_AMT_GOODS_PRICE].AMT_GOODS_PRICE, data = df1)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')
plt.subplot(1,2,2)
plt.title('On-Time Payments')
sns.scatterplot(x = df0[df0['AMT_ANNUITY'] < max_value0_AMT_ANNUITY].AMT_ANNUITY, y = df0[df0['AMT_GOODS_PRICE'] < max_value0_AMT_GOODS_PRICE].AMT_GOODS_PRICE, data = df0)
plt.ticklabel_format(style='plain', axis='x')
plt.ticklabel_format(style='plain', axis='y')
plt.tight_layout(pad = 4)
In [ ]:
# AMT_ANNUITY and AMT_GOODS_PRICE have strοng pοsitive cοrrelatiοn. This means that as Annuity increases, sο dοes Gοοds Price
In [216]:
# plot to check male Vs female default rate
plt.figure(figsize = (20,4))
# For male
plt.subplot(1,2,1)
plt.title("NAME_CONTRACT_TYPE and TARGET for Male")
sns.countplot(x='NAME_CONTRACT_TYPE', hue="TARGET", data=app_df[app_df['CODE_GENDER'] == "M"])
# For female
plt.subplot(1,2,2)
plt.title("NAME_CONTRACT_TYPE and TARGET for Female")
sns.countplot(x='NAME_CONTRACT_TYPE', hue="TARGET", data=app_df[app_df['CODE_GENDER'] == "F"])
Out[216]:
<Axes: title={'center': 'NAME_CONTRACT_TYPE and TARGET for Female'}, xlabel='NAME_CONTRACT_TYPE', ylabel='count'>
In [ ]:
# Male applicants are defaulting mοre that female applicants
In [234]:
# creating a HeatMap to view the correlations above 80% and 99.99%
for i in app_df.columns:
if i.startswith("FLAG"):
app_df.drop(columns=i, inplace=True)
corr_df1 = df1.select_dtypes(include=["int64","float64"]).corr()
plt.figure(figsize = (25,25))
sns.heatmap(data = corr_df1, annot = True, cbar = True, fmt='.2f')
plt.show()
In [250]:
# getting top 10 correlations for Payment Difficulties
corr_df1[corr_df1 <= 0.99].unstack().sort_values(ascending = False).head(20)
Out[250]:
FLOORSMAX_MODE FLOORSMAX_MEDI 0.989195
FLOORSMAX_MEDI FLOORSMAX_MODE 0.989195
YEARS_LAST_PHONE_CHANGE DAYS_LAST_PHONE_CHANGE 0.988086
DAYS_LAST_PHONE_CHANGE YEARS_LAST_PHONE_CHANGE 0.988086
FLOORSMAX_MODE FLOORSMAX_AVG 0.986594
FLOORSMAX_AVG FLOORSMAX_MODE 0.986594
AMT_CREDIT AMT_GOODS_PRICE 0.983103
AMT_GOODS_PRICE AMT_CREDIT 0.983103
YEARS_BEGINEXPLUATATION_AVG YEARS_BEGINEXPLUATATION_MODE 0.980466
YEARS_BEGINEXPLUATATION_MODE YEARS_BEGINEXPLUATATION_AVG 0.980466
YEARS_BEGINEXPLUATATION_MEDI 0.978073
YEARS_BEGINEXPLUATATION_MEDI YEARS_BEGINEXPLUATATION_MODE 0.978073
REGION_RATING_CLIENT_W_CITY REGION_RATING_CLIENT 0.956637
REGION_RATING_CLIENT REGION_RATING_CLIENT_W_CITY 0.956637
CNT_FAM_MEMBERS CNT_CHILDREN 0.885484
CNT_CHILDREN CNT_FAM_MEMBERS 0.885484
DEF_60_CNT_SOCIAL_CIRCLE DEF_30_CNT_SOCIAL_CIRCLE 0.868994
DEF_30_CNT_SOCIAL_CIRCLE DEF_60_CNT_SOCIAL_CIRCLE 0.868994
LIVE_REGION_NOT_WORK_REGION REG_REGION_NOT_WORK_REGION 0.847885
REG_REGION_NOT_WORK_REGION LIVE_REGION_NOT_WORK_REGION 0.847885
dtype: float64
In [ ]:
the top 10 correlation are:
AMT_GOODS_PRICE AMT_CREDIT - 0.98
REGION_RATING_CLIENT REGION_RATING_CLIENT_W_CITY - 0.96
CNT_FAM_MEMBERS CNT_CHILDREN - 0.89
DEF_60_CNT_SOCIAL_CIRCLE DEF_30_CNT_SOCIAL_CIRCLE - 0.87
REG_REGION_NOT_WORK_REGION LIVE_REGION_NOT_WORK_REGION - 0.85
LIVE_CITY_NOT_WORK_CITY REG_CITY_NOT_WORK_CITY - 0.78
AMT_ANNUITY AMT_GOODS_PRICE - 0.75
AMT_ANNUITY AMT_CREDIT - 0.75
DAYS_EMPLOYED FLAG_DOCUMENT_6 - 0.62
DAYS_BIRTH DAYS_EMPLOYED - 0.58
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